Machine-learning architectures for broadcast and multicast communications

ABSTRACT

Techniques and apparatuses are described for machine-learning architectures for broadcast and multicast communications. In implementations, a network entity determines a configuration of a deep neural network (DNN) for processing broadcast or multicast communications transmitted over a wireless communication system, where the communications are directed to a targeted group of user equipments (UEs). The network entity forms a network-entity DNN based on the determined configuration of the DNN and processes the broadcast or multicast communications using the network-entity DNN. In implementations, the network entity forms a common DNN to process and/or propagate the broadcast or multicast communications to the targeted group of UEs.

BACKGROUND

The evolution of wireless communication systems oftentimes stems from ademand for data throughput. As one example, the demand for dataincreases as more and more devices gain access to wireless communicationsystems. Evolving devices also execute data-intensive applications thatutilize more data than traditional applications, such as data-intensivestreaming-video applications, data-intensive social media applications,data-intensive audio services, etc. Thus, to accommodate increased datausage, evolving wireless communication systems utilize increasinglycomplex architectures to provide more data throughput relative to legacywireless communication systems.

As one example, fifth generation (5G) standards and technologiestransmit data using higher frequency bands, such as the above-6Gigahertz (GHz) band (e.g., 5G millimeter wave (mmW) technologies) toincrease data capacity. However, transmitting and recovering informationusing these higher frequency ranges poses challenges. To illustrate,higher frequency signals are more susceptible to multipath fading,scattering, atmospheric absorption, diffraction, interference, and soforth, relative to lower-frequency signals. These signal distortionsoftentimes lead to errors when recovering the information at a receiver.As another example, hardware capable of transmitting, receiving,routing, and/or otherwise using these higher frequencies can be complexand expensive, which increases the processing costs in awirelessly-networked device.

SUMMARY

This document describes techniques and apparatuses for machine-learningarchitectures for broadcast and multicast communications. Inimplementations, a network entity determines a configuration of a deepneural network (DNN) for processing broadcast or multicastcommunications transmitted over a wireless communication system, wherethe communications are directed to a targeted group of user equipments(UEs). The network entity forms a network-entity DNN based on thedetermined configuration of the DNN and processes the broadcast ormulticast communications using the network-entity DNN. Inimplementations, the network entity forms a common DNN to process and/orpropagate the broadcast or multicast communications to the targetedgroup of UEs.

The details of one or more implementations of machine-learningarchitectures for broadcast and multicast communications are set forthin the accompanying drawings and the following description. Otherfeatures and advantages will be apparent from the description anddrawings, and from the claims. This summary is provided to introducesubject matter that is further described in the Detailed Description andDrawings. Accordingly, this summary should not be considered to describeessential features nor used to limit the scope of the claimed subjectmatter.

BRIEF DESCRIPTION OF THE DRAWINGS

The details of one or more aspects of machine-learning architectures forbroadcast and multicast communications are described below. The use ofthe same reference numbers in different instances in the description andthe figures indicate similar elements:

FIG. 1 illustrates an example environment in which various aspects ofmachine-learning architectures for broadcast and multicastcommunications can be implemented.

FIG. 2 illustrates an example device diagram of devices that canimplement various aspects of machine-learning architectures forbroadcast and multicast communications.

FIG. 3 illustrates an example device diagram of a device that canimplement various aspects of machine-learning architectures forbroadcast and multicast communications.

FIG. 4 illustrates an example machine-learning module that can implementvarious aspects of machine-learning architectures for broadcast andmulticast communications.

FIG. 5 illustrates example block diagrams of processing chains utilizedby devices to process communications transmitted over a wirelesscommunication system.

FIG. 6 illustrates an example operating environment in which multipledeep neural networks are utilized in a wireless communication system.

FIG. 7 illustrates an example transaction diagram between variousdevices for configuring a neural network using a neural networkformation configuration.

FIG. 8 illustrates an example of generating multiple neural networkformation configurations.

FIG. 9 illustrates an example transaction diagram between variousdevices for communicating neural network formation configurations.

FIG. 10 illustrates an example environment in which various aspects ofmachine-learning architectures for broadcast and multicastcommunications can be implemented.

FIG. 11 illustrates an example environment in which various aspects ofmachine-learning architectures for broadcast and multicastcommunications can be implemented.

FIG. 12 illustrates an example environment in which various aspects ofmachine-learning architectures for broadcast and multicastcommunications can be implemented.

FIG. 13 illustrates an example transaction diagram between variousdevices in accordance with various implementations of machine-learningarchitectures for broadcast and multicast communications.

FIG. 14 illustrates an example transaction diagram between variousdevices in accordance with various implementations of machine-learningarchitectures for broadcast and multicast communications.

FIG. 15 illustrates an example transaction diagram between variousdevices in accordance with various implementations of machine-learningarchitectures for broadcast and multicast communications.

FIG. 16 illustrates an example transaction diagram between variousdevices in accordance with various implementations of machine-learningarchitectures for broadcast and multicast communications.

FIG. 17 illustrates an example method for using machine-learningarchitectures for broadcast and multicast communications.

FIG. 18 illustrates an example method for using machine-learningarchitectures for broadcast and multicast communications.

DETAILED DESCRIPTION

In conventional wireless communication systems, transmitter and receiverprocessing chains include complex functionality. For instance, a channelestimation block in the processing chain estimates or predicts how asignal distorts while propagating through a transmission environment. Asanother example, channel equalizer blocks reverse the signal distortionsidentified by the channel estimation block. These complex functionsoftentimes become more complicated when processing higher frequencyranges, such as 5G mmW signals that are at or around the 6 GHz band.

DNNs provide alternative solutions to complex processing, such as thecomplex functionality used in a wireless communication system. Bytraining a DNN on transmitter and/or receiver processing chainoperations, the DNN can replace conventional functionality in a varietyof ways, such as by replacing some or all of the conventional processingblocks used to process broadcast or multicast communication signals,replacing individual processing chain blocks, etc. Dynamicreconfiguration of a DNN, such as by modifying various parameterconfigurations (e.g., coefficients, layer connections, kernel sizes)also provides an ability to adapt to changing operating conditions.

This document describes aspects of machine-learning architectures forbroadcast and multicast communications. In implementations, a networkentity associated with a wireless communication system determines aconfiguration of a deep neural network (DNN) for processing broadcast ormulticast communications transmitted over a wireless communicationsystem to a targeted group of UEs. In some implementations, the networkentity, such as a core network server or a base station (BS), determinesthe configuration based on various characteristics associated with thetargeted group of UEs, such as an estimated location or UE capabilities.The network entity forms a network-entity DNN based on the determinedconfiguration of the DNN and processes the broadcast or multicastcommunications using the network-entity DNN to transmit the broadcast ormulticast communications over the wireless communication system anddirected to the targeted group of UEs.

One or more aspects of machine-learning architectures for broadcast andmulticast communications include processing broadcast or multicastcommunications using a DNN to direct the broadcast or multicastcommunications to a targeted group of UEs over a wireless communicationsystem. In implementations, a network entity receives feedback from atleast one UE of the targeted group of UEs and determines a modificationto the DNN based on the feedback. The network entity then transmits anindication of the modification to the targeted group of UEs. Alternatelyor additionally, the network entity updates the DNN with themodification to form a modified DNN and processes the broadcast ormulticast communications using the modified DNN to transmit thebroadcast or multicast communications over the wireless communicationsystem and directed to the targeted group of UEs.

Processing broadcast and multicast communications using DNN(s) allowsvarious devices operating in the wireless communication system tocorrect for changes in a current operating condition, such as locationchanges of a targeted UE. By monitoring the changes, such as throughmetrics or feedback from a UE, the DNN(s) can be adjusted to correct oraddress the changes. Alternately or additionally, the DNN(s) can bemodified based upon capabilities of the targeted UEs. Thesemodifications improve an overall performance (e.g., lower bit errors,improved signal quality, improved latency) of how the broadcast andmulticast communications are transmitted and/or recovered. As anotherexample, DNNs can be trained to process complex input that correspondsto a complex environment, such a complex environment that includes atargeted group of UEs, where each UE has different processing powersand/or estimated locations. Accordingly, DNNs provide a flexible andmodifiable solution to complex processing.

The phrases “transmitted over,” “communications exchanged,” and“communications associated with” include generating communications to betransmitted over the wireless communication system (e.g. processingpre-transmission communications) and/or processing communicationsreceived over the wireless communication system. Thus, “processingcommunications transmitted over the wireless communication system,”“communications exchanged over the wireless communication system,” aswell as “communications associated with the wireless communicationsystem” include generating the transmissions (e.g., pre-transmissionprocessing), processing received transmissions, or any combinationthereof.

Example Environment

FIG. 1 illustrates an example environment 100 which includes a userequipment 110 (UE 110) that can communicate with base stations 120(illustrated as base stations 121 and 122) through one or more wirelesscommunication links 130 (wireless link 130), illustrated as wirelesslinks 131 and 132. For simplicity, the UE 110 is implemented as asmartphone but may be implemented as any suitable computing orelectronic device, such as a mobile communication device, modem,cellular phone, gaming device, navigation device, media device, laptopcomputer, desktop computer, tablet computer, smart appliance,vehicle-based communication system, or an Internet-of-Things (IoT)device such as a sensor or an actuator. The base stations 120 (e.g., anEvolved Universal Terrestrial Radio Access Network Node B, E-UTRAN NodeB, evolved Node B, eNodeB, eNB, Next Generation Node B, gNode B, gNB,ng-eNB, or the like) may be implemented in a macrocell, microcell, smallcell, picocell, and the like, or any combination thereof.

The base stations 120 communicate with the user equipment 110 using thewireless links 131 and 132, which may be implemented as any suitabletype of wireless link. The wireless links 131 and 132 include controland data communication, such as downlink of data and control informationcommunicated from the base stations 120 to the user equipment 110,uplink of other data and control information communicated from the userequipment 110 to the base stations 120, or both. The wireless links 130may include one or more wireless links (e.g., radio links) or bearersimplemented using any suitable communication protocol or standard, orcombination of communication protocols or standards, such as 3rdGeneration Partnership Project Long-Term Evolution (3GPP LTE), FifthGeneration New Radio (5G NR), and so forth. Multiple wireless links 130may be aggregated in a carrier aggregation to provide a higher data ratefor the UE 110. Multiple wireless links 130 from multiple base stations120 may be configured for Coordinated Multipoint (CoMP) communicationwith the UE 110.

The base stations 120 are collectively a Radio Access Network 140 (e.g.,RAN, Evolved Universal Terrestrial Radio Access Network, E-UTRAN, 5G NRRAN or NR RAN). The base stations 121 and 122 in the RAN 140 areconnected to a core network 150. The base stations 121 and 122 connect,at 102 and 104 respectively, to the core network 150 through an NG2interface for control-plane signaling and using an NG3 interface foruser-plane data communications when connecting to a 5G core network, orusing an S1 interface for control-plane signaling and user-plane datacommunications when connecting to an Evolved Packet Core (EPC) network.The base stations 121 and 122 can communicate using an Xn ApplicationProtocol (XnAP) through an Xn interface, or using an X2 ApplicationProtocol (X2AP) through an X2 interface, at 106, to exchange user-planeand control-plane data. The user equipment 110 may connect, via the corenetwork 150, to public networks, such as the Internet 160 to interactwith a remote service 170. The remote service 170 represents thecomputing, communication, and storage devices used to provide any of amultitude of services including interactive voice or videocommunication, file transfer, streaming voice or video, and othertechnical services implemented in any manner such as voice calls, videocalls, website access, messaging services (e.g., text messaging ormulti-media messaging), photo file transfer, enterprise softwareapplications, social media applications, video gaming, streaming videoservices, and podcasts.

Example Devices

FIG. 2 illustrates an example device diagram 200 of the user equipment110 and one of the base stations 120. FIG. 3 illustrates an exampledevice diagram 300 of a core network server 302. The user equipment 110,the base station 120, and/or the core network server 302 may includeadditional functions and interfaces that are omitted from FIG. 2 or FIG.3 for the sake of clarity.

The user equipment 110 includes antennas 202, a radio frequency frontend 204 (RF front end 204), a wireless transceiver (e.g., an LTEtransceiver 206, and/or a 5G NR transceiver 208) for communicating withthe base station 120 in the RAN 140. The RF front end 204 of the userequipment 110 can couple or connect the LTE transceiver 206, and the 5GNR transceiver 208 to the antennas 202 to facilitate various types ofwireless communication. The antennas 202 of the user equipment 110 mayinclude an array of multiple antennas that are configured similar to ordifferently from each other. The antennas 202 and the RF front end 204can be tuned to, and/or be tunable to, one or more frequency bandsdefined by the 3GPP LTE and 5G NR communication standards andimplemented by the LTE transceiver 206, and/or the 5G NR transceiver208. Additionally, the antennas 202, the RF front end 204, the LTEtransceiver 206, and/or the 5G NR transceiver 208 may be configured tosupport beamforming for the transmission and reception of communicationswith the base station 120. By way of example and not limitation, theantennas 202 and the RF front end 204 can be implemented for operationin sub-gigahertz bands, sub-6 GHz bands, and/or above 6 GHz bands thatare defined by the 3GPP LTE and 5G NR communication standards.

The user equipment 110 also includes processor(s) 210 andcomputer-readable storage media 212 (CRM 212). The processor 210 may bea single core processor or a multiple core processor composed of avariety of materials, such as silicon, polysilicon, high-K dielectric,copper, and so on. The computer-readable storage media described hereinexcludes propagating signals. CRM 212 may include any suitable memory orstorage device such as random-access memory (RAM), static RAM (SRAM),dynamic RAM (DRAM), non-volatile RAM (NVRAM), read-only memory (ROM), orFlash memory useable to store device data 214 of the user equipment 110.The device data 214 includes user data, multimedia data, beamformingcodebooks, applications, neural network tables, and/or an operatingsystem of the user equipment 110, which are executable by processor(s)210 to enable user-plane communication, control-plane signaling, anduser interaction with the user equipment 110.

In some implementations, the computer-readable storage media 212includes a neural network table 216 that stores various architectureand/or parameter configurations that form a neural network, such as, byway of example and not of limitation, parameters that specify afully-connected layer neural network architecture, a convolutional layerneural network architecture, a recurrent neural network layer, a numberof connected hidden neural network layers, an input layer architecture,an output layer architecture, a number of nodes utilized by the neuralnetwork, coefficients (e.g., weights and biases) utilized by the neuralnetwork, kernel parameters, a number of filters utilized by the neuralnetwork, strides/pooling configurations utilized by the neural network,an activation function of each neural network layer, interconnectionsbetween neural network layers, neural network layers to skip, and soforth. Accordingly, the neural network table 216 includes anycombination of NN formation configuration elements (e.g., architectureand/or parameter configurations) that can be used to create a NNformation configuration (e.g., a combination of one or more NN formationconfiguration elements) that defines and/or forms a DNN. In someimplementations, a single index value of the neural network table 216maps to a single NN formation configuration element (e.g., a 1:1correspondence). Alternately or additionally, a single index value ofthe neural network table 216 maps to a NN formation configuration (e.g.,a combination of NN formation configuration elements). In someimplementations, the neural network table includes input characteristicsfor each NN formation configuration element and/or NN formationconfiguration, where the input characteristics describe properties aboutthe training data used to generate the NN formation configurationelement and/or NN formation configuration as further described.

In some implementations, the CRM 212 may also include a user equipmentneural network manager 218 (UE neural network manager 218). Alternatelyor additionally, the UE neural network manager 218 may be implemented inwhole or part as hardware logic or circuitry integrated with or separatefrom other components of the user equipment 110. The UE neural networkmanager 218 accesses the neural network table 216, such as by way of anindex value, and forms a DNN using the NN formation configurationelements specified by a NN formation configuration. In implementations,UE neural network manager forms multiple DNNs to process wirelesscommunications (e.g., downlink communications and/or uplinkcommunications exchanged with the base station 120).

The device diagram for the base station 120, shown in FIG. 2 , includesa single network node (e.g., a gNode B). The functionality of the basestation 120 may be distributed across multiple network nodes or devicesand may be distributed in any fashion suitable to perform the functionsdescribed herein. The base station 120 include antennas 252, a radiofrequency front end 254 (RF front end 254), one or more wirelesstransceivers (e.g. one or more LTE transceivers 256, and/or one or more5G NR transceivers 258) for communicating with the UE 110. The RF frontend 254 of the base station 120 can couple or connect the LTEtransceivers 256 and the 5G NR transceivers 258 to the antennas 252 tofacilitate various types of wireless communication. The antennas 252 ofthe base station 120 may include an array of multiple antennas that areconfigured similar to, or different from, each other. The antennas 252and the RF front end 254 can be tuned to, and/or be tunable to, one ormore frequency band defined by the 3GPP LTE and 5G NR communicationstandards, and implemented by the LTE transceivers 256, and/or the 5G NRtransceivers 258. Additionally, the antennas 252, the RF front end 254,the LTE transceivers 256, and/or the 5G NR transceivers 258 may beconfigured to support beamforming, such as Massive-Multiple-In, MultipleOut (Massive-MIMO), for the transmission and reception of communicationswith the UE 110.

The base station 120 also include processor(s) 260 and computer-readablestorage media 262 (CRM 262). The processor 260 may be a single coreprocessor or a multiple core processor composed of a variety ofmaterials, such as silicon, polysilicon, high-K dielectric, copper, andso on. CRM 262 may include any suitable memory or storage device such asrandom-access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM),non-volatile RAM (NVRAM), read-only memory (ROM), or Flash memoryuseable to store device data 264 of the base station 120. The devicedata 264 includes network scheduling data, radio resource managementdata, beamforming codebooks, applications, and/or an operating system ofthe base station 120, which are executable by processor(s) 260 to enablecommunication with the user equipment 110.

CRM 262 also includes a base station manager 266. Alternately oradditionally, the base station manager 266 may be implemented in wholeor part as hardware logic or circuitry integrated with or separate fromother components of the base station 120. In at least some aspects, thebase station manager 266 configures the LTE transceivers 256 and the 5GNR transceivers 258 for communication with the user equipment 110, aswell as communication with a core network, such as the core network 150.

CRM 262 also includes a base station neural network manager 268 (BSneural network manager 268). Alternately or additionally, the BS neuralnetwork manager 268 may be implemented in whole or part as hardwarelogic or circuitry integrated with or separate from other components ofthe base station 120. In at least some aspects, the BS neural networkmanager 268 selects the NN formation configurations utilized by the basestation 120 and/or UE 110 to configure deep neural networks forprocessing wireless communications, such as by selecting a combinationof NN formation configuration elements. In some implementations, the BSneural network manager receives feedback from the UE 110, and selectsthe neural network formation configuration based on the feedback.Alternately or additionally, the BS neural network manager 268 receivesneural network formation configuration directions from core network 150elements through a core network interface 276 or an inter-base stationinterface 274 and forwards the neural network formation configurationdirections to UE 110.

CRM 262 includes training module 270 and neural network table 272. Inimplementations, the base station 120 manage and deploy NN formationconfigurations to UE 110. Alternately or additionally, the base station120 maintain the neural network table 272. The training module 270teaches and/or trains DNNs using known input data. For instance, thetraining module 270 trains DNN(s) for different purposes, such asprocessing communications transmitted over a wireless communicationsystem (e.g., encoding downlink communications, modulating downlinkcommunications, demodulating downlink communications, decoding downlinkcommunications, encoding uplink communications, modulating uplinkcommunications, demodulating uplink communications, decoding uplinkcommunications). This includes training the DNN(s) offline (e.g., whilethe DNN is not actively engaged in processing the communications) and/oronline (e.g., while the DNN is actively engaged in processing thecommunications).

In implementations, the training module 270 extracts learned parameterconfigurations from the DNN to identify the NN formation configurationelements and/or NN formation configuration, and then adds and/or updatesthe NN formation configuration elements and/or NN formationconfiguration in the neural network table 272. The extracted parameterconfigurations include any combination of information that defines thebehavior of a neural network, such as node connections, coefficients,active layers, weights, biases, pooling, etc.

The neural network table 272 stores multiple different NN formationconfiguration elements and/or NN formation configurations generatedusing the training module 270. In some implementations, the neuralnetwork table includes input characteristics for each NN formationconfiguration element and/or NN formation configuration, where the inputcharacteristics describe properties about the training data used togenerate the NN formation configuration element and/or NN formationconfiguration. For instance, the input characteristics includes, by wayof example and not of limitation, power information,signal-to-interference-plus-noise ratio (SINR) information, channelquality indicator (CQI) information, channel state information (CSI),Doppler feedback, frequency bands, BLock Error Rate (BLER), Quality ofService (QoS), Hybrid Automatic Repeat reQuest (HARD) information (e.g.,first transmission error rate, second transmission error rate, maximumretransmissions), latency, Radio Link Control (RLC), Automatic RepeatreQuest (ARQ) metrics, received signal strength (RSS), uplink SINR,timing measurements, error metrics, UE capabilities, base stationcapabilities (BS capabilities), power mode, Internet Protocol (IP) layerthroughput, end2end latency, end2end packet loss ratio, etc.Accordingly, the input characteristics include, at times, Layer 1, Layer2, and/or Layer 3 metrics. In some implementations, a single index valueof the neural network table 272 maps to a single NN formationconfiguration element (e.g., a 1:1 correspondence). Alternately oradditionally, a single index value of the neural network table 272 mapsto a NN formation configuration (e.g., a combination of NN formationconfiguration elements).

In implementations, the base station 120 synchronizes the neural networktable 272 with the neural network table 216 such that the NN formationconfiguration elements and/or input characteristics stored in one neuralnetwork table is replicated in the second neural network table.Alternately or additionally, the base station 120 synchronizes theneural network table 272 with the neural network table 216 such that theNN formation configuration elements and/or input characteristics storedin one neural network table represent complementary functionality in thesecond neural network table (e.g., NN formation configuration elementsfor transmitter path processing in the first neural network table, NNformation configuration elements for receiver path processing in thesecond neural network table).

The base station 120 also include an inter-base station interface 274,such as an Xn and/or X2 interface, which the base station manager 266configures to exchange user-plane, control-plane, and other informationbetween other base station 120, to manage the communication of the basestation 120 with the user equipment 110. The base station 120 include acore network interface 276 that the base station manager 266 configuresto exchange user-plane, control-plane, and other information with corenetwork functions and/or entities.

In FIG. 3 , the core network server 302 may provide all or part of afunction, entity, service, and/or gateway in the core network 150. Eachfunction, entity, service, and/or gateway in the core network 150 may beprovided as a service in the core network 150, distributed acrossmultiple servers, or embodied on a dedicated server. For example, thecore network server 302 may provide the all or a portion of the servicesor functions of a User Plane Function (UPF), an Access and MobilityManagement Function (AMF), a Serving Gateway (S-GW), a Packet DataNetwork Gateway (P-GW), a Mobility Management Entity (MME), an EvolvedPacket Data Gateway (ePDG), and so forth. The core network server 302 isillustrated as being embodied on a single server that includesprocessor(s) 304 and computer-readable storage media 306 (CRM 306). Theprocessor 304 may be a single core processor or a multiple coreprocessor composed of a variety of materials, such as silicon,polysilicon, high-K dielectric, copper, and so on. CRM 306 may includeany suitable memory or storage device such as random-access memory(RAM), static RAM (SRAM), dynamic RAM (DRAM), non-volatile RAM (NVRAM),read-only memory (ROM), hard disk drives, or Flash memory useful tostore device data 308 of the core network server 302. The device data308 includes data to support a core network function or entity, and/oran operating system of the core network server 302, which are executableby processor(s) 304.

CRM 306 also includes one or more core network applications 310, which,in one implementation, is embodied on CRM 306 (as shown). The one ormore core network applications 310 may implement the functionality suchas UPF, AMF, S-GW, P-GW, MME, ePDG, and so forth. Alternately oradditionally, the one or more core network applications 310 may beimplemented in whole or part as hardware logic or circuitry integratedwith or separate from other components of the core network server 302.

CRM 306 also includes a core network neural network manager 312 thatmanages NN formation configurations used to process communicationsexchanged between UE 110 and the base stations 120. In someimplementations, the core network neural network manager 312 analyzesvarious parameters, such as current signal channel conditions (e.g., asreported by base stations 120, as reported by other wireless accesspoints, as reported by UEs 110 (via base stations or other wirelessaccess points)), capabilities at base stations 120 (e.g., antennaconfigurations, cell configurations, Multiple-In, Multiple-Out (MIMO),capabilities, radio capabilities, processing capabilities), capabilitiesof UE 110 (e.g., antenna configurations, MIMO capabilities, radiocapabilities, processing capabilities), and so forth. For example, thebase stations 120 obtain the various parameters during thecommunications with the UE and forward the parameters to the corenetwork neural network manager 312. The core network neural networkmanager selects, based on these parameters, a NN formation configurationthat improves the accuracy of a DNN processing the communications.Improving the accuracy signifies an improved accuracy in the output,such as lower bit errors, generated by the neural network relative to aneural network configured with another NN formation configuration. Thecore network neural network manager 312 then communicates the selectedNN formation configuration to the base stations 120 and/or the UE 110.In implementations, the core network neural network manager 312 receivesUE and/or BS feedback from the base station 120 and selects an updatedNN formation configuration based on the feedback.

CRM 306 includes training module 314 and neural network table 316. Inimplementations, the core network server 302 manages and deploys NNformation configurations to multiple devices in a wireless communicationsystem, such as UEs 110 and base stations 120. Alternately oradditionally, the core network server maintains the neural network table316 outside of the CRM 306. The training module 314 teaches and/ortrains DNNs using known input data. For instance, the training module314 trains DNN(s) to process different types of pilot communicationstransmitted over a wireless communication system. This includes trainingthe DNN(s) offline and/or online. In implementations, the trainingmodule 314 extracts a learned NN formation configuration and/or learnedNN formation configuration elements from the DNN and stores the learnedNN formation configuration elements in the neural network table 316.Thus, a NN formation configuration includes any combination ofarchitecture configurations (e.g., node connections, layer connections)and/or parameter configurations (e.g., weights, biases, pooling) thatdefine or influence the behavior of a DNN. In some implementations, asingle index value of the neural network table 316 maps to a single NNformation configuration element (e.g., a 1:1 correspondence).Alternately or additionally, a single index value of the neural networktable 316 maps to a NN formation configuration (e.g., a combination ofNN formation configuration elements).

In some implementations, the training module 314 of the core networkneural network manager 312 generates complementary NN formationconfigurations and/or NN formation configuration elements to thosestored in the neural network table 216 at the UE 110 and/or the neuralnetwork table 272 at the base station 121. As one example, the trainingmodule 314 generates neural network table 316 with NN formationconfigurations and/or NN formation configuration elements that have ahigh variation in the architecture and/or parameter configurationsrelative to medium and/or low variations used to generate the neuralnetwork table 272 and/or the neural network table 216. For instance, theNN formation configurations and/or NN formation configuration elementsgenerated by the training module 314 correspond to fully-connectedlayers, a full kernel size, frequent sampling and/or pooling, highweighting accuracy, and so forth. Accordingly, the neural network table316 includes, at times, high accuracy neural networks at the trade-offof increased processing complexity and/or time.

The NN formation configurations and/or NN formation configurationelements generated by the training module 270 have, at times, more fixedarchitecture and/or parameter configurations (e.g., fixed connectionlayers, fixed kernel size, etc.), and less variation, relative to thosegenerated by the training module 314. The training module 270, forexample, generates streamlined NN formation configurations (e.g., fastercomputation times, less data processing), relative to those generated bythe training module 314, to optimize or improve a performance of end2endnetwork communications at the base station 121 and/or the UE 110.Alternately or additionally, the NN formation configurations and/or NNformation configuration elements stored at the neural network table 216at the UE 110 include more fixed architecture and/or parameterconfigurations, relative to those stored in the neural network table 316and/or the neural network table 272, that reduce requirements (e.g.,computation speed, less data processing points, less computations, lesspower consumption, etc.) at the UE 110 relative to the base station 121and/or the core network server 302. In implementations, the variationsin fixed (or flexible) architecture and/or parameter configurations ateach neural network are based on the processing resources (e.g.,processing capabilities, memory constraints, quantization constraints(e.g., 8-bit vs. 16-bit), fixed-point vs. floating point computations,floating point operations per second (FLOPS), power availability) of thedevices targeted to form the corresponding DNNs. Thus, UEs or accesspoints with less processing resources relative to a core network serveror base station receive NN formation configurations optimized for theavailable processing resources.

The neural network table 316 stores multiple different NN formationconfiguration elements generated using the training module 314. In someimplementations, the neural network table includes input characteristicsfor each NN formation configuration element and/or NN formationconfiguration, where the input characteristics describe properties aboutthe training data used to generate the NN formation configuration. Forinstance, the input characteristics can include power information, SINRinformation, CQI, CSI, Doppler feedback, RSS, error metrics, minimumend-to-end (E2E) latency, desired E2E latency, E2E QoS, E2E throughput,E2E packet loss ratio, cost of service, etc.

CRM 306 also includes an end-to-end machine-learning controller 318 (E2EML controller 318). The E2E ML controller 318 determines an end-to-endmachine-learning configuration (E2E ML configuration) for processinginformation exchanged through an E2E communication, such as a QoS flow.In implementations, the E2E ML controller analyzes any combination of MLcapabilities (e.g., supported ML architectures, supported number oflayers, available processing power, memory limitations, available powerbudget, fixed-point processing vs. floating point processing, maximumkernel size capability, computation capability) of devices participatingin the E2E communication. Alternately or additionally, the E2E MLcontroller analyzes any combination of QoS requirements, QoS parameters,and/or QoS characteristics to determine an E2E ML configuration thatsatisfies the associated requirements, parameters, and/orcharacteristics. In some implementations, the E2E ML controller obtainsmetrics that characterize a current operating environment and analyzesthe current operating environment to determine the E2E ML configuration.This includes determining an E2E ML configuration that includes anarchitecture configuration in combination with parameterconfiguration(s) that define a DNN or determining an E2E MLconfiguration that simply includes parameter configurations used toupdate the DNN.

In determining the E2E ML configuration, the E2E ML controller sometimesdetermines a partitioned E2E ML configuration that distributes theprocessing functionality associated with the E2E ML configuration acrossmultiple devices. For clarity, FIG. 3 illustrates the end-to-endmachine-learning controller 318 as separate from the core network neuralnetwork manager 312, but in alternate or additional implementations, thecore network neural network manager 312 includes functionality performedby the end-to-end machine-learning controller 318 or vice versa.Further, while FIG. 3 illustrates the core network server 302implementing the E2E ML controller 318, alternate or additional devicescan implement the E2E ML controller, such as the base station 120 and/orother network elements.

The core network server 302 also includes a network-slice manager 320.Generally speaking, the network-slice manager 190 uses network slicingto provide different quality-of-service flows through the wirelesscommunication network (e.g., provide different quality-of-service flowsbetween at least one UE 110, at least one base station 120, and the corenetwork 150). At times, the network-slice manager 320 works inconjunction with the E2E ML controller 318 to partition networkresources to provide communication exchanges that meet or exceed aquality-of-service level. For example, the quality-of-service level canbe specified through one or more quality-of-service parameters, such aslatency, throughput (e.g., bandwidth or data rate), reliability, or anerror rate (e.g., a bit error rate). Other example quality-of-serviceparameters include availability, packet loss, or jitter. In addition tothe quality-of-service level, the network slice can also provide aparticular level of security through cryptography. In someimplementations, the network-slice manager 320 associates each networkslice with one or more end-to-end machine-learning architectures toprovide the quality-of-service level. For clarity, FIG. 3 illustratesthe network-slice manager 320 as separate from the core network neuralnetwork manager 312 and the E2E ML controller 318, but in alternate oradditional implementations, the core network neural network manager 312includes functionality performed by the network-slice manager 320 orvice versa. Further, while FIG. 3 illustrates the core network server302 implementing the network-slice manager 320, alternate or additionaldevices can implement the network-slice manager 320, such as the basestation 120 and/or other network elements.

The core network server 302 also includes a core network interface 322for communication of user-plane, control-plane, and other informationwith the other functions or entities in the core network 150, basestations 120, or UE 110. In implementations, the core network server 302communicates NN formation configurations to the base station 120 usingthe core network interface 322. The core network server 302 alternatelyor additionally receives feedback from the base stations 120 and/or theUE 110, by way of the base stations 120, using the core networkinterface 322.

Having described an example environment and example devices that can beutilized for neural network formation configuration feedback in wirelesscommunications, consider now a discussion of configurablemachine-learning modules that is in accordance with one or moreimplementations.

Configurable Machine-Learning Modules

FIG. 4 illustrates an example machine-learning module 400. Themachine-learning module 400 implements a set of adaptive algorithms thatlearn and identify patterns within data. The machine-learning module 400can be implemented using any combination of software, hardware, and/orfirmware.

In FIG. 4 , the machine-learning module 400 includes a deep neuralnetwork 402 (DNN 402) with groups of connected nodes (e.g., neuronsand/or perceptrons) that are organized into three or more layers. Thenodes between layers are configurable in a variety of ways, such as apartially-connected configuration where a first subset of nodes in afirst layer are connected with a second subset of nodes in a secondlayer, a fully-connected configuration where each node in a first layerare connected to each node in a second layer, etc. A neuron processesinput data to produce a continuous output value, such as any real numberbetween 0 and 1. In some cases, the output value indicates how close theinput data is to a desired category. A perceptron performs linearclassifications on the input data, such as a binary classification. Thenodes, whether neurons or perceptrons, can use a variety of algorithmsto generate output information based upon adaptive learning. Using theDNN, the machine-learning module 400 performs a variety of differenttypes of analysis, including single linear regression, multiple linearregression, logistic regression, step-wise regression, binaryclassification, multiclass classification, multi-variate adaptiveregression splines, locally estimated scatterplot smoothing, and soforth.

In some implementations, the machine-learning module 400 adaptivelylearns based on supervised learning. In supervised learning, themachine-learning module 400 receives various types of input data astraining data. The machine-learning module 400 processes the trainingdata to learn how to map the input to a desired output. As one example,the machine-learning module 400 receives digital samples of a signal asinput data and learns how to map the signal samples to binary data thatreflects information embedded within the signal. As another example, themachine-learning module 400 receives binary data as input data andlearns how to map the binary data to digital samples of a signal withthe binary data embedded within the signal. During a training procedure,the machine-learning module 400 uses labeled or known data as an inputto the DNN. The DNN analyzes the input using the nodes and generates acorresponding output. The machine-learning module 400 compares thecorresponding output to truth data and adapts the algorithms implementedby the nodes to improve the accuracy of the output data. Afterwards, theDNN applies the adapted algorithms to unlabeled input data to generatecorresponding output data.

The machine-learning module 400 uses statistical analyses and/oradaptive learning to map an input to an output. For instance, themachine-learning module 400 uses characteristics learned from trainingdata to correlate an unknown input to an output that is statisticallylikely within a threshold range or value. This allows themachine-learning module 400 to receive complex input and identify acorresponding output. Some implementations train the machine-learningmodule 400 on characteristics of communications transmitted over awireless communication system (e.g., time/frequency interleaving,time/frequency deinterleaving, convolutional encoding, convolutionaldecoding, power levels, channel equalization, inter-symbol interference,quadrature amplitude modulation/demodulation, frequency-divisionmultiplexing/de-multiplexing, transmission channel characteristics).This allows the trained machine-learning module 400 to receive samplesof a signal as an input, such as samples of a downlink signal receivedat a user equipment, and recover information from the downlink signal,such as the binary data embedded in the downlink signal.

In FIG. 4 , the DNN includes an input layer 404, an output layer 406,and one or more hidden layer(s) 408 that are positioned between theinput layer 404 and the output layer 406. Each layer has an arbitrarynumber of nodes, where the number of nodes between layers can be thesame or different. In other words, input layer 404 can have a samenumber and/or different number of nodes as output layer 406, outputlayer 406 can have a same number and/or different number of nodes thanhidden layer(s) 408, and so forth.

Node 410 corresponds to one of several nodes included in input layer404, where the nodes perform independent computations from one another.As further described, a node receives input data, and processes theinput data using algorithm(s) to produce output data. At times, thealgorithm(s) include weights and/or coefficients that change based onadaptive learning. Thus, the weights and/or coefficients reflectinformation learned by the neural network. Each node can, in some cases,determine whether to pass the processed input data to the next node(s).To illustrate, after processing input data, node 410 can determinewhether to pass the processed input data to node 412 and/or node 414 ofhidden layer(s) 408. Alternately or additionally, node 410 passes theprocessed input data to nodes based upon a layer connectionarchitecture. This process can repeat throughout multiple layers untilthe DNN generates an output using the nodes of output layer 406.

A neural network can also employ a variety of architectures thatdetermine what nodes within the neural network are connected, how datais advanced and/or retained in the neural network, what weights andcoefficients are used to process the input data, how the data isprocessed, and so forth. These various factors collectively describe aNN formation configuration. To illustrate, a recurrent neural network,such as a long short-term memory (LSTM) neural network, forms cyclesbetween node connections in order to retain information from a previousportion of an input data sequence. The recurrent neural network thenuses the retained information for a subsequent portion of the input datasequence. As another example, a feed-forward neural network passesinformation to forward connections without forming cycles to retaininformation. While described in the context of node connections, it isto be appreciated that the NN formation configuration can include avariety of parameter configurations that influence how the neuralnetwork processes input data.

A NN formation configuration of a neural network can be characterized byvarious architecture and/or parameter configurations. To illustrate,consider an example in which the DNN implements a convolutional neuralnetwork. Generally, a convolutional neural network corresponds to a typeof DNN in which the layers process data using convolutional operationsto filter the input data. Accordingly, the convolutional NN formationconfiguration can be characterized with, by way of example and not oflimitation, pooling parameter(s), kernel parameter(s), weights, and/orlayer parameter(s).

A pooling parameter corresponds to a parameter that specifies poolinglayers within the convolutional neural network that reduce thedimensions of the input data. To illustrate, a pooling layer can combinethe output of nodes at a first layer into a node input at a secondlayer. Alternately or additionally, the pooling parameter specifies howand where in the layers of data processing the neural network poolsdata. A pooling parameter that indicates “max pooling,” for instance,configures the neural network to pool by selecting a maximum value fromthe grouping of data generated by the nodes of a first layer, and usethe maximum value as the input into the single node of a second layer. Apooling parameter that indicates “average pooling” configures the neuralnetwork to generate an average value from the grouping of data generatedby the nodes of the first layer and use the average value as the inputto the single node of the second layer.

A kernel parameter indicates a filter size (e.g., a width and height) touse in processing input data. Alternately or additionally, the kernelparameter specifies a type of kernel method used in filtering andprocessing the input data. A support vector machine, for instance,corresponds to a kernel method that uses regression analysis to identifyand/or classify data. Other types of kernel methods include Gaussianprocesses, canonical correlation analysis, spectral clustering methods,and so forth. Accordingly, the kernel parameter can indicate a filtersize and/or a type of kernel method to apply in the neural network.

Weight parameters specify weights and biases used by the algorithmswithin the nodes to classify input data. In implementations, the weightsand biases are learned parameter configurations, such as parameterconfigurations generated from training data.

A layer parameter specifies layer connections and/or layer types, suchas a fully-connected layer type that indicates to connect every node ina first layer (e.g., output layer 406) to every node in a second layer(e.g., hidden layer(s) 408), a partially-connected layer type thatindicates which nodes in the first layer to disconnect from the secondlayer, an activation layer type that indicates which filters and/orlayers to activate within the neural network, and so forth. Alternatelyor additionally, the layer parameter specifies types of node layers,such as a normalization layer type, a convolutional layer type, apooling layer type, etc.

While described in the context of pooling parameters, kernel parameters,weight parameters, and layer parameters, it is to be appreciated thatother parameter configurations can be used to form a DNN withoutdeparting from the scope of the claimed subject matter. Accordingly, aNN formation configuration can include any other type of parameter thatcan be applied to a DNN that influences how the DNN processes input datato generate output data.

Some implementations configure machine-learning module 400 based on acurrent operating environment. To illustrate, consider amachine-learning module trained to generate binary data from digitalsamples of a signal. A transmission environment oftentimes modifies thecharacteristics of a signal traveling through the environment.Transmission environments oftentimes change, which impacts how theenvironment modifies the signal. A first transmission environment, forinstance, modifies a signal in a first manner, while a secondtransmission environment modifies the signal in a different manner thanthe first. These differences impact an accuracy of the output resultsgenerated by a machine-learning module. For instance, a neural networkconfigured to process communications transmitted over the firsttransmission environment may generate errors when processingcommunications transmitted over the second transmission environment(e.g., bit errors that exceed a threshold value).

Various implementations generate and store NN formation configurationsand/or NN formation configuration elements (e.g., various architectureand/or parameter configurations) for different transmissionenvironments. Base stations 120 and/or core network server 302, forexample, train the machine-learning module 400 using any combination ofBS neural network manager 268, training module 270, core network neuralnetwork manager 312, and/or training module 314. The training can occuroffline when no active communication exchanges are occurring, or onlineduring active communication exchanges. For example, the base stations120 and/or core network server 302 can mathematically generate trainingdata, access files that store the training data, obtain real-worldcommunications data, etc. The base stations 120 and/or core networkserver 302 then extract and store the various learned NN formationconfigurations in a neural network table. Some implementations storeinput characteristics with each NN formation configuration, where theinput characteristics describe various properties of the transmissionenvironment corresponding to the respective NN formation configuration.In implementations, a neural network manager selects a NN formationconfiguration and/or NN formation configuration element(s) by matching acurrent transmission environment and/or current operating environment tothe input characteristics.

Having described configurable machine-learning modules, consider now adiscussion of deep neural networks in wireless communication systemsthat is in accordance with one or more implementations.

Deep Neural Networks in Wireless Communication Systems

Wireless communication systems include a variety of complex componentsand/or functions, such as the various devices and modules described withreference to the example environment 100 of FIG. 1 , the example devicediagram 200 of FIG. 2 , and the example device diagram 300 of FIG. 3 .In some implementations, the devices participating in the wirelesscommunication system chain together a series of functions to enable theexchange of information over wireless connections.

To demonstrate, FIG. 5 illustrates example block diagram 500 and exampleblock diagram 502, each of which depicts an example processing chainutilized by devices in a wireless communication system. For simplicity,the block diagrams illustrate high-level functionality, and it is to beappreciated that the block diagrams may include additional functionsthat are omitted from FIG. 5 for the sake of clarity.

In the upper portion of FIG. 5 , block diagram 500 includes atransmitter block 504 and a receiver block 506. Transmitter block 504includes a transmitter processing chain that progresses from top tobottom. The transmitter processing chain begins with input data thatprogresses to an encoding stage, followed by a modulating stage, andthen a radio frequency (RF) analog transmit (Tx) stage. The encodingstage can include any type and number of encoding stages employed by adevice to transmit data over the wireless communication system.

To illustrate, an encoding stage receives binary data as input, andprocesses the binary data using various encoding algorithms to appendinformation to the binary data, such as frame information. Alternatelyor additionally, the encoding stage transforms the binary data, such asby applying forward error correction that adds redundancies to helpinformation recovery at a receiver. As another example, the encodingstage converts the binary data into symbols.

An example modulating stage receives an output generated by the encodingstage as input and embeds the input onto a signal. For instance, themodulating stage generates digital samples of signal(s) embedded withthe input from the encoding stage. Thus, in transmitter block 504, theencoding stage and the modulating stage represent a high-leveltransmitter processing chain that often includes lower-level complexfunctions, such as convolutional encoding, serial-to-parallelconversion, cyclic prefix insertion, channel coding, time/frequencyinterleaving, and so forth. The RF analog Tx stage receives the outputfrom the modulating stage, generates an analog RF signal based on themodulating stage output, and transmits the analog RF signal to receiverblock 506.

Receiver block 506 performs complementary processing relative totransmitter block 504 using a receiver processing chain. The receiverprocessing chain illustrated in receiver block 506 progresses from topto bottom and includes an RF analog receive (Rx) stage, followed by ademodulating stage, and a decoding stage.

The RF analog Rx stage receives signals transmitted by the transmitterblock 504, and generates a signal used by the demodulating stage. As oneexample, the RF analog Rx stage includes a down-conversion componentand/or an analog-to-digital converter (ADC) to generate samples of thereceived signal. The demodulating stage processes input from the RFanalog Rx stage to extract data embedded on the signal (e.g., dataembedded by the modulating stage of the transmitter block 504). Thedemodulating stage, for instance, recovers symbols and/or binary data.

The decoding stage receives input from the demodulating stage, such asrecovered symbols and/or binary data, and processes the input to recoverthe transmitted information. To illustrate, the decoding stage correctsfor data errors based on forward error correction applied at thetransmitter block, extracts payload data from frames and/or slots, andso forth. Thus, the decoding stage generates the recovered information.

As noted, the transmitter and receiver processing chains illustrated bytransmitter block 504 and receiver block 506 have been simplified forclarity and can include multiple complex modules. At times, thesemodules are specific to particular functions and/or conditions.Consider, for example, a receiver processing chain that processesOrthogonal Frequency Division Modulation (OFDM) transmissions. Torecover information from OFDM transmissions, the receiver block 506oftentimes includes multiple processing blocks, each of which isdedicated to a particular function, such as an equalization block thatcorrects for distortion in a received signal, a channel estimation blockthat estimates transmission channel properties to identify the effectson a transmission due to scattering, power decay, and so forth. At highfrequencies, such as 5G mmW signals in the 6 GHz band, these blocks canbe computationally and/or monetarily expensive (e.g., requiresubstantial processing power, require expensive hardware). Further,implementing blocks that generate outputs with an accuracy within adesired threshold oftentimes requires more specific and less flexiblecomponents. To illustrate, an equalization block that functions for 5GmmW signals in the 6 GHz band may not perform with the same accuracy atother frequency bands, thus necessitating different equalization blocksfor different bands and adding complexity to the corresponding devices.

Some implementations include DNNs in the transmission and/or receiverprocessing chains. In block diagram 502, transmitter block 508 includesone or more deep neural network(s) 510 (DNNs 510) in the transmitterprocessing chain, while receiver block 512 includes one or more deepneural network(s) 514 (DNNs 514) in the receiver processing chain.

For simplicity, the DNNs 510 in the transmitter block 508 correspond tothe encoding stage and the modulating stage of transmitter block 504. Itis to be appreciated, however, that the DNNs 510 can perform anyhigh-level and/or low-level operation found within the transmitterprocessing chain. For instance, a first DNN performs low-leveltransmitter-side forward error correction, a second DNN performslow-level transmitter-side convolutional encoding, and so forth.Alternately or additionally, the DNNs 510 perform high-level processing,such as end-to-end processing that corresponds to the encoding stage andthe modulating stage of transmitter block 508.

In a similar manner, the DNNs 514 in receiver block 512 perform receiverprocessing chain functionality (e.g., demodulating stage, decodingstage). The DNNs 514 can perform any high-level and/or low-leveloperation found within the receiver processing chain, such as low-levelreceiver-side bit error correction, low-level receiver-side symbolrecovery, high-level end-to-end demodulating and decoding, etc.Accordingly, DNNs 514 in wireless communication systems can beconfigured to replace high-level operations and/or low-level operationsin transmitter and receiver processing chains. At times, the DNNs 514performing the high-level operations and/or low-level operations can beconfigured and/or reconfigured based on a current operating environmentas further described. This DNN reconfigurability, along with DNNcoefficient updates, provides more flexibility and adaptability to theprocessing chains relative to the more specific and less flexiblecomponents.

Some implementations process communication exchanges over the wirelesscommunication system using multiple DNNs, where each DNN has arespective purpose (e.g., uplink processing, downlink processing, uplinkencoding processing, downlink decoding processing, etc.). Todemonstrate, consider now FIG. 6 that illustrates an example operatingenvironment 600 that includes UE 110 and base station 120. Inimplementations, the UE 110 and base station 120 exchange communicationswith one another over a wireless communication system by processing thecommunications using multiple DNNs.

In FIG. 6 , the base station neural network manager 268 of the basestation 120 includes a downlink processing module 602 for processingdownlink communications, such as for generating downlink communicationstransmitted to the UE 110. To illustrate, the base station neuralnetwork manager 268 forms deep neural network(s) 604 (DNNs 604) in thedownlink processing module 602 using NN formation configurations asfurther described. In some examples, the DNNs 604 correspond to the DNNs510 of FIG. 5 . In other words, the DNNs 604 perform some or all of thetransmitter processing functionality used to generate downlinkcommunications.

Similarly, the UE neural network manager 218 of the UE 110 includes adownlink processing module 606, where the downlink processing module 606includes deep neural network(s) 608 (DNNs 608) for processing (received)downlink communications. In various implementations, the UE neuralnetwork manager 218 forms the DNNs 608 using NN formationconfigurations. In FIG. 6 , the DNNs 608 correspond to the DNNs 514 ofFIG. 5 , where the deep neural network(s) 606 of UE 110 perform some orall receiver processing functionality for (received) downlinkcommunications. Accordingly, the DNNs 604 and the DNNs 608 performcomplementary processing to one another (e.g., encoding/decoding,modulating/demodulating).

The DNNs 604 and/or DNNs 608 can include multiple deep neural networks,where each DNN is dedicated to a respective channel, a respectivepurpose, and so forth. The base station 120, as one example, processesdownlink control channel information using a first DNN of the DNNs 604,processes downlink data channel information using a second DNN of theDNNs 604, and so forth. As another example, the UE 110 processesdownlink control channel information using a first DNN of the DNNs 608,processes downlink data channel information using a second DNN of theDNNs 608, etc.

The base station 120 and/or the UE 110 also process uplinkcommunications using DNNs. In environment 600, the UE neural networkmanager 218 includes an uplink processing module 610, where the uplinkprocessing module 610 includes deep neural network(s) 612 (DNNs 612) forgenerating and/or processing uplink communications (e.g., encoding,modulating). In other words, uplink processing module 610 processespre-transmission communications as part of processing the uplinkcommunications. The UE neural network manager 218, for example, formsthe DNNs 612 using NN formation configurations. At times, the DNNs 612correspond to the DNNs 510 of FIG. 5 . Thus, the DNNs 612 perform someor all of the transmitter processing functionality used to generateuplink communications transmitted from the UE 110 to the base station120.

Similarly, uplink processing module 614 of the base station 120 includesdeep neural network(s) 616 (DNNs 616) for processing (received) uplinkcommunications, where base station neural network manager 268 forms DNNs616 using NN formation configurations as further described. In examples,the DNNs 616 of the base station 120 correspond to the DNNs 514 of FIG.5 , and perform some or all receiver processing functionality for(received) uplink communications, such as uplink communications receivedfrom UE 110. At times, the DNNs 612 and the DNNs 616 performcomplementary functionality of one another. Alternately or additionally,the uplink processing module 610 and/or the uplink processing module 614include multiple DNNs, where each DNN has a dedicated purpose (e.g.,processes a respective channel, performs respective uplinkfunctionality, and so forth). FIG. 6 illustrates the DNNs 604, 608, 612,and 616 as residing within the respective neural network managers tosignify that the neural network managers form the DNNs, and it is to beappreciated that the DNNs can be formed external to the neural networkmanagers (e.g., UE neural network manager 218 and base station neuralnetwork manager 268) within different components, processing chains,modules, etc.

Having described deep neural networks in wireless communication systems,consider now a discussion of signaling and control transactions over awireless communication system that can be used to configure deep neuralnetworks for downlink and uplink communications that is in accordancewith one or more implementations.

Signaling and Control Transactions to Configure Deep Neural Networks

FIG. 7 illustrates an example signaling and control transaction diagram700 between a base station and a user equipment in accordance with oneor more aspects of neural network formation configurations in wirelesscommunication. Alternate or additional implementations includetransactions that include a core network server. For example, the corenetwork server 302 performs, in some implementations, various signalingand control actions performed by the base station 120 as illustrated byFIG. 7 . The signaling and control transactions may be performed by thebase station 120 and the UE 110 of FIG. 1 using elements of FIGS. 1-6 .For clarity, FIG. 7 omits the core network server 302, but alternateimplementations include the core network server as further described.

As illustrated, at 705, the UE 110 optionally indicates UE capabilities(e.g., capabilities supported by the UE) to a network entity, such asthe base station 120. In some implementations, the UE capabilitiesinclude ML-related capabilities, such as a maximum kernel sizecapability, a memory limitation, a computation capability, supported MLarchitectures, supported number of layers, available processing power,memory limitation, available power budget, and fixed-point processingversus floating point processing. At times, the base station forwardsthe UE capabilities to a core network server (e.g., the core networkserver 302).

At 710 the base station 120 determines a neural network formationconfiguration. In determining the neural network formationconfiguration, the base station analyzes any combination of information,such as a channel type being processed by the deep neural network (e.g.,downlink, uplink, data, control, etc.), transmission medium properties(e.g., power measurements, signal-to-interference-plus-noise ratio(SINR) measurements, channel quality indicator (CQI) measurements),encoding schemes, UE capabilities, BS capabilities, and so forth. Insome implementations, the base station 120 determines the neural networkformation configuration based upon the UE capabilities indicated at 705.Alternately or additionally, the base station 120 obtains the UEcapabilities from a networked storage device, such as a server. In someimplementations, the core network server 302 determines the neuralnetwork formation configuration in manner(s) similar to that describedwith respect to the base station, and communicates the determined neuralnetwork formation configuration to the base station.

The base station 120, for instance, receives message(s) from the UE 110(not shown) that indicates one or more capabilities of the UE, such as,by way of example and not of limitation, connectivity information,dual-connectivity information, carrier aggregation capabilities,downlink physical parameter values, uplink physical parameter values,supported downlink/uplink categories, inter-frequency handover, andML-capabilities (e.g., a maximum kernel size capability, a memorylimitation, a computation capability, supported ML architectures,supported number of layers, available processing power, memorylimitation, available power budget, fixed-point processing vs. floatingpoint processing). The base station 120 (and/or the core network server302) identifies, from the message(s), the UE capabilities that impacthow the UE processes communications, and/or how the base stationprocesses communications from the UE and selects a neural networkformation configuration with improved output accuracy relative to otherneural network formation configurations.

In some implementations, the base station 120 (and/or the core networkserver 302) selects the neural network formation configuration frommultiple neural network formation configurations. Alternately oradditionally, the base station 120 (and/or the core network server 302)selects the neural network formation configuration by selecting a subsetof neural network architecture formation elements in a neural networktable. At times, the base station 120 (and/or the core network server302) analyzes multiple neural network formation configurations and/ormultiple neural network formation configuration elements included in aneural network table, and determines the neural network formationconfiguration by selects and/or creates a neural network formationconfiguration that aligns with current channel conditions, such as bymatching the channel type, transmission medium properties, etc., toinput characteristics as further described. Alternately or additionally,the base station 120 (and/or the core network server 302) selects theneural network formation configuration based on network parameters, suchas scheduling parameters (e.g., scheduling Multiple User, MultipleInput, Multiple Output (MU-MIMO) for downlink communications, schedulingMU-MIMO for uplink communications).

At 715, the base station 120 communicates the neural network formationconfiguration to the UE 110. Alternately or additionally, the corenetwork server 302 communicates the neural network formationconfiguration to the base station 120, and the base station 120 forwardsthe neural network formation configuration to the UE 110. In someimplementations, the base station transmits a message that specifies theneural network formation configuration, such as by transmitting amessage that includes an index value that maps to an entry in a neuralnetwork table, such as neural network table 216 of FIG. 2 . Alternatelyor additionally, the base station transmits a message that includesneural network parameter configurations (e.g., weight values,coefficient values, number of filters). In some cases, the base station120 specifies a purpose and/or processing assignment in the message,where the processing assignment indicates what channels, and/or where ina processing chain, the configured neural network applies to, such as adownlink control channel processing, an uplink data channel processing,downlink decoding processing, uplink encoding processing, etc.Accordingly, the base station can communicate a processing assignmentwith a neural network formation configuration.

In some implementations, the base station 120 communicates multipleneural network formation configurations to the UE 110. For example, thebase station transmits a first message that directs the UE to use afirst neural network formation configuration for uplink encoding, and asecond message that directs the UE to use a second neural networkformation configuration for downlink decoding. In some scenarios, thebase station 120 communicates multiple neural network formationconfigurations, and the respective processing assignments, in a singlemessage. As yet another example, the base station communicates themultiple neural network formation configurations using different radioaccess technologies (RATs). The base station can, for instance, transmita first neural network formation configuration for downlinkcommunication processing to the UE 110 using a first RAT and/or carrier,and transmit a second neural network formation configuration for uplinkcommunication processing to the UE 110 using a second RAT and/orcarrier.

At 720, the UE 110 forms a first neural network based on the neuralnetwork formation configuration. For instance, the UE 110 accesses aneural network table using the index value(s) communicated by the basestation to obtain the neural network formation configuration and/or theneural network formation configuration elements. Alternately oradditionally, the UE 110 extracts neural network architecture and/orparameter configurations from the message. The UE 110 then forms theneural network using the neural network formation configuration, theextracted architecture and/or parameter configurations, etc. In someimplementations, the UE processes all communications using the firstneural network, while in other implementations, the UE processes selectcommunications using the first neural network based on a processingassignment.

At 725, the base station 120 communicates information based on theneural network formation configuration. For instance, with reference toFIG. 6 , the base station 120 processes downlink communications using asecond neural network configured with complementary functionality to thefirst neural network. In other words, the second neural network uses asecond neural network formation configuration that is complementary tothe neural network formation configuration. In turn, at 730, the UE 110recovers the information using the first neural network.

Having described signaling and control transactions that can be used toconfigure neural networks for processing communications, consider now adiscussion of generating and communicating neural network formationconfigurations that is in accordance with one or more implementations.

Generating and Communicating Neural Network Formation Configurations

In supervised learning, machine-learning modules process labeledtraining data to generate an output. The machine-learning modulesreceive feedback on an accuracy of the generated output and modifyprocessing parameters to improve the accuracy of the output. FIG. 8illustrates an example 800 that describes aspects of generating multipleNN formation configurations. At times, various aspects of the example800 are implemented by any combination of training module 270, basestation neural network manager 268, core network neural network manager312, and/or training module 314 of FIGS. 2 and 3 .

The upper portion of FIG. 8 includes machine-learning module 400 of FIG.4 . In implementations, a neural network manager determines to generatedifferent NN formation configurations. To illustrate, consider ascenario in which the base station neural network manager 268 determinesto generate a NN formation configuration by selecting a combination ofNN formation configuration elements from a neural network table, wherethe NN formation configuration corresponds to a UE decoding and/ordemodulating downlink communications. In other words, the NN formationconfiguration (by way of the combination of NN formation configurationelements) forms a DNN that processes downlink communications received bya UE. Oftentimes, however, transmission channel conditions vary which,in turn, affects the characteristics of the downlink communications. Forinstance, a first transmission channel distorts the downlinkcommunications by introducing frequency offsets, a second transmissionchannel distorts the downlink communications by introducing Dopplereffects, a third transmission channel distorts the downlinkcommunications by introducing multipath channel effects, and so forth.To accurately process the downlink communications (e.g., reduce biterrors), various implementations select multiple NN formationconfigurations, where each NN formation configuration (and associatedcombination of NN formation configuration elements) corresponds to arespective input condition, such as a first transmission channel, asecond transmission channel, etc.

Training data 802 represents an example input to the machine-learningmodule 400. In FIG. 8 , the training data 802 represents datacorresponding to a downlink communication. Training data 802, forinstance, can include digital samples of a downlink communicationssignal, recovered symbols, recovered frame data, etc. In someimplementations, the training module generates the training datamathematically or accesses a file that stores the training data. Othertimes, the training module obtains real-world communications data. Thus,the training module can train the machine-learning module 400 usingmathematically generated data, static data, and/or real-world data. Someimplementations generate input characteristics 804 that describe variousqualities of the training data, such as transmission channel metrics, UEcapabilities, UE velocity, and so forth.

Machine-learning module 400 analyzes the training data, and generates anoutput 806, represented here as binary data. Some implementationsiteratively train the machine-learning module 400 using the same set oftraining data and/or additional training data that has the same inputcharacteristics 804 to improve the accuracy of the machine-learningmodule 400. During training, the machine-learning module 400 modifiessome or all of the architecture and/or parameter configurations of aneural network included in the machine-learning module 400, such as nodeconnections, coefficients, kernel sizes, etc. At some point in thetraining, the training module determines to extract the architectureand/or parameter configurations 808 of the neural network (e.g., poolingparameter(s), kernel parameter(s), layer parameter(s), weights), such aswhen the training module determines that the accuracy meets or exceeds adesired threshold, the training process meets or exceeds an iterationnumber, and so forth. The training module then extracts the architectureand/or parameter configurations from the machine-learning module 400 touse as a NN formation configuration and/or NN formation configurationelement(s). The architecture and/or parameter configurations can includeany combination of fixed architecture and/or parameter configurations,and/or variable architectures and/or parameter configurations.

The lower portion of FIG. 8 includes neural network table 810 thatrepresents a collection of NN formation configuration elements, such asneural network table 216, neural network table 272, and/or neuralnetwork table 316 of FIG. 2 and FIG. 3 . The neural network table 810stores various combinations of architecture configurations, parameterconfigurations 808, and input characteristics 804, but alternateimplementations exclude the input characteristics 804 from the table.Various implementations update and/or maintain the NN formationconfiguration elements and/or the input characteristics 804 as themachine-learning module 400 learns additional information. For example,at index 812, the neural network manager and/or the training moduleupdates neural network table 810 to include architecture and/orparameter configurations 808 generated by the machine-learning module400 while analyzing the training data 802.

The neural network manager and/or the training module alternately oradditionally adds the input characteristics 804 to the neural networktable 810 and links the input characteristics 804 to the architectureand/or parameter configurations 808. This allows the inputcharacteristics 804 to be obtained at a same time as the architectureand/or parameter configurations 808, such as through using an indexvalue that references into the neural network table 810 (e.g.,references NN formation configurations, references NN formationconfiguration elements). In some implementations, the neural networkmanager selects a NN formation configuration by matching the inputcharacteristics to a current operating environment, such as by matchingthe input characteristics to current channel conditions, UEcapabilities, UE characteristics (e.g., velocity, location, etc.) and soforth.

Having described generating and communicating neural network formationconfigurations, consider now a discussion of signaling and controltransactions over a wireless communication system that can be used tocommunicate neural network formation configurations that is inaccordance with one or more implementations

Signaling and Control Transactions to Communicate Neural NetworkFormation Configurations

FIG. 9 illustrates an example signaling and control transaction diagram900 between a base station and a user equipment in accordance with oneor more aspects of communicating neural network formationconfigurations, such as communicating a NN formation configuration. Inimplementations, the signaling and control transactions may be performedby any one of the base stations 120 and the UE 110 of FIG. 1 , usingelements of FIGS. 1-8 . In alternate or additional implementations, thesignaling and control transactions may include interactions with thecore network server 302 of FIG. 3 (not shown in FIG. 9 ). Inimplementations, portions or all of the signaling and controltransactions described with reference to the signaling and controltransaction diagram 900 correspond to signaling and control transactionsdescribed with reference to FIG. 7 .

As illustrated, at 905 the base station 120 maintains a neural networktable. Alternately or additionally, the core network server 302maintains a neural network table. For example, a neural network manager(base station neural network manager 268, core network neural networkmanager 312) and/or a training module (training module 270, trainingmodule 314) generate and/or maintain a neural network table (e.g.,neural network table 272, neural network table 316) using anycombination of mathematically generated training data, data extractedfrom real-world communications, files, etc. In various implementations,the base station 120 (and/or the core network server 302) maintainsmultiple neural network tables, where each neural network table includesmultiple neural network formation configurations and/or neural networkformation configuration elements for a designated purpose, such as afirst neural network table designated for data channel communications, asecond neural network table designated for control channelcommunications, and so forth.

At 910, the base station 120 transmits the neural network table to theUE 110. In some implementations, the base station 120 first receives theneural network table from the core network server 302). As one example,the base station transmits the neural network table using layer 3messaging (e.g., Radio Resource Control (RRC) messages). In transmittingthe neural network table, the base station transmits any combination ofarchitecture and/or parameter configurations that can be used to form adeep neural network, examples of which are provided in this disclosure.Alternately or additionally the base station transmits an indicationwith the neural network table that designates a processing assignmentfor the neural network table. Accordingly, the base station transmitsmultiple neural network tables to the UE, with a respective processingassignment designated for each neural network table. In someimplementations, the base station 120 broadcasts the neural networktable(s) to a group of UEs. Other times, the base station 120 transmitsa UE-dedicated neural network table to the UE 110.

At 915, the base station 120 identifies a neural network formationconfiguration to use in processing communications. Alternately oradditionally, the core network server 302 identifies the neural networkformation configuration, and communicates the neural network formationconfiguration to the base station 120. For example, the base stationdetermines a neural network formation configuration to use in processingthe communications by selecting a combination of neural networkformation architecture elements, such as that described at 710 of FIG. 7, by analyzing any combination of information, such as a channel typebeing processed by a deep neural network (e.g., downlink, uplink, data,control, etc.), transmission medium properties (e.g., powermeasurements, signal-to-interference-plus-noise ratio (SINR)measurements, channel quality indicator (CQI) measurements), encodingschemes, UE capabilities, BS capabilities, and so forth. In someimplementations, the base station 120 identifies a default neuralnetwork formation configuration to use as the neural network formationconfiguration. Thus, identifying the neural network formationconfiguration can include identifying default neural network formationconfiguration and/or updates to a neural network formationconfiguration.

In identifying the neural network formation configuration, the basestation 121 (and/or the core network server 302) ascertains a neuralnetwork formation configuration in the neural network table thatcorresponds to the determined neural network formation configuration. Inother words, the base station 120 identifies a neural network formationconfiguration and/or neural network formation configuration elements inneural network table 272 and/or neural network table 216 of FIG. 2 thatalign with the determined neural network formation configuration, suchas by correlating and or matching input characteristics. In identifyingthe neural network formation configuration and/or neural networkformation configuration elements in the neural network table, the basestation identifies index value(s) of the neural network formationconfiguration and/or neural network formation configuration elements.

At 920, the base station 120 transmits an indication that directs the UE110 to form a deep neural network using a neural network formationconfiguration from the neural network table. For example, similar tothat described at 715 of FIG. 7 and/or at 840 of FIG. 8 , the basestation 120 communicates the index value(s) to the UE 110, and directsthe UE 110 to form the deep neural network using the neural networkformation configuration indicated by the index value(s). The basestation can transmit the indication to the UE in any suitable manner. Asone example, the base station transmits index value(s) that correspondsto the neural network formation configuration using a layer 2 message(e.g., a Radio Link Control (RLC) message, Medium Access Control (MAC)control element(s)). In some implementations, the base station comparesa current operating environment (e.g., one or more of channelconditions, UE capabilities, BS capabilities, metrics) to inputcharacteristics stored within the neural network table and identifiesstored input characteristics aligned with the current operatingenvironment. In turn, the base station obtains the index value of storedinput characteristics which, in turn, provides the index value of theneural network formation configuration and/or neural network formationconfiguration elements. The base station 120 then transmits the indexvalue(s) as the indication. At times, the base station 120 includes aprocessing assignment to indicate a position in a processing chain toapply the deep neural network. In some implementations, the base stationtransmits the index value(s) and/or processing assignment using adownlink control channel.

At times, the base station transmits rule(s) to the UE specifyingoperating parameters related to applying the neural network formationconfiguration. In one example, the rules include a time instance thatindicates when to process communications with the deep neural networkformed using the neural network formation configuration. Alternately oradditionally, the rules specify a time threshold value that directs theUE to use a default neural network formation configuration instead ofthe specified neural network formation configuration when a data channeland control channel are within the time threshold value. Additionally,or alternatively, a rule may direct the user equipment to use the sameneural network formation configuration for data channel communicationsand control channel communications when a data channel and controlchannel are within the time threshold value. To illustrate, consider anexample in which the UE processes data channel communications using afirst deep neural network (formed using a first neural network formationconfiguration), and control channel communications using a second deepneural network (formed using a second neural network formationconfiguration). If the data channel communications and control channelcommunications fall within the time threshold value specified by thetime instance, the UE processes both channels using a default deepneural network (formed with the default neural network formationconfiguration) and/or the same deep neural network, since there may notbe enough time to switch between the first deep neural network and thesecond deep neural network.

At times, the base station specifies a default neural network formationconfiguration to UE(s) using a downlink control channel to communicatethe default neural network formation configuration, where the defaultneural network formation configuration forms a deep neural network thatprocesses a variety of input data. In some implementations, the defaultneural network formation configuration forms a deep neural network thatprocesses the variety of input data with an accuracy within a thresholdrange. The default neural network formation configuration can include ageneric neural network formation configuration.

To illustrate, some implementations generate or select neural networkformation configurations for specific operating conditions, such as afirst neural network formation configuration specific to UE downlinkcontrol channel processing (e.g., demodulating and/or deciding) with acurrent operating environment “X”, a second neural network formationconfiguration specific to UE downlink control channel processing with acurrent operating environment “Y”, and so forth. For example, a firstneural network formation configuration can correlate to a currentoperating environment in which a detected interference level is high, asecond neural network formation configuration can correlate to a currentoperating environment in which a detected interference level is low, athird neural network formation configuration can correlate to a currentoperating environment in which a connected UE appears stationary, afourth neural network formation configuration can correlate to a currentoperating environment in which the connected UE appears to be moving andwith a particular velocity, and so forth.

Forming a deep neural network using a neural network formationconfiguration for specific operating conditions improves (relative toforming the deep neural network with other neural network formationconfigurations) an accuracy of the output generated by the deep neuralnetwork when processing input data corresponding to the specificoperating conditions. However, this introduces a tradeoff insofar as thedeep neural network formed with the neural network formationconfiguration for specific operating conditions generates output withless accuracy when processing input associated with other operatingconditions. Conversely, a default neural network formation configurationcorresponds to a neural network formation configuration that processes awider variety of input, such as a variety of input that spans moreoperating conditions. In other words, a deep neural network configuredwith a default neural network formation configuration processes a largervariety of communications relative to neural network formationconfigurations directed to specific operating conditions.

At 925, the UE 110 forms the deep neural network using the neuralnetwork formation configuration. The UE, as one example, extracts theindex value(s) transmitted by the base station 120, and obtains theneural network formation configuration and/or neural network formationconfiguration elements by accessing the neural network table using theindex value(s). Alternately or additionally, the UE 110 extracts theprocessing assignment, and forms the deep neural network in theprocessing chain as specified by the processing assignment.

At 930, the base station 120 transmits communications to the UE 110,such as downlink data channel communications. At 935, the UE 110processes the communications using the deep neural network. Forinstance, the UE 110 processes the downlink data channel communicationsusing the deep neural network to recover the data. As another example,processing the communications includes processing a reply to thecommunications, where the UE 110 processes, using the deep neuralnetwork, uplink communications in reply to the downlink communications.

Having described signaling and control transactions that can be used tocommunicate neural network formation configurations, consider now adiscussion of E2E ML for wireless networks that is in accordance withone or more implementations.

E2E ML for Wireless Networks

Aspects of an end-to-end communication (E2E communication) involve twoendpoints exchanging information over a communication path, such asthrough a wireless network. At times, the E2E communication performs asingle-directional exchange of information, where a first endpoint sendsinformation and a second endpoint receives the information. Other times,the E2E communication performs bi-directional exchanges of information,where both endpoints send and receive the information. The endpoints ofan E2E communication can include any entity capable of consuming and/orgenerating the information, such as a computing device, an application,a service, and so forth. To illustrate, consider an example in which anapplication executing at a UE exchanges information with a remoteservice over a wireless network. For this example, the E2E communicationcorresponds to the communication path between the application and theremote service, where the application and the remote service act asendpoints.

While the E2E communication involves endpoints that exchangeinformation, the E2E communication alternately or additionally includesintermediate, entities (e.g., devices, applications, services) thatparticipate in the exchange of information. To illustrate, consideragain the example of an E2E communication established through a wirelessnetwork where an application at a UE functions as a first endpoint and aremote service functions as a second endpoint. In establishing the E2Ecommunication between the endpoints, the wireless network utilizes anycombination of UE(s), base station(s), core network server(s), remotenetwork(s), remote service(s), and so forth, such as that described withreference to the environment 100 of FIG. 1 . Thus, intermediaryentities, such as the base station 120 and the core network 150,participate in establishing the E2E communication and/or participatingin the E2E communication to enable an exchange of information betweenthe endpoints.

Different factors impact the operational efficiency of the E2Ecommunication and how the network elements process information exchangedthrough the E2E communication. For instance, with reference to an E2Ecommunication established using a wireless network, a current operatingenvironment (e.g., current channel conditions, UE location, UE movement,UE capabilities) impacts how accurately (e.g., bit error rate, packetloss) a receiving endpoint recovers the information. As one example, anE2E communication implemented using 5G mmW technologies becomessusceptible to more signal distortions relative to lower frequency sub-6GHz signals as further described.

As another example, various implementations partition wireless networkresources differently based on an end-to-end analysis of an E2Ecommunication, where the wireless network resources include anycombination of, by way of example and not of limitation, physicalhardware, physical spectrum, logical channels, network functions,services provided, quality of service, latency, and so forth. Wirelessnetwork-resource partitioning allows the wireless network to dynamicallyallocate the wireless network resources based on an expected usage toimprove an efficiency of how the wireless network resources are used(e.g., reduce the occurrence of unused and/or wasted resources). Toillustrate, consider a variety of devices connecting to a wirelessnetwork, where the devices have different performance requirementsrelative to one another (e.g., a first device has secure data transferrequirements, a second device has high priority/low latency datatransfer requirements, a third device has high data rate requirements).For at least some devices, a fixed and/or static distribution ofwireless network resources (e.g., a fixed configuration for the wirelessnetwork resources used to implement an E2E communication) can lead tounused resources and/or fail to meet the performance requirements ofsome services. Thus, partitioning the wireless network resources canimprove an overall efficiency of how the wireless network resources areutilized. However, the partitioning causes variations in how one pair ofE2E endpoints exchanges information relative to a second pair of E2Eendpoints.

To further demonstrate, consider a Quality-of-Service flow (QoS flow)that corresponds to information exchanged in a wireless network. Invarious implementations, an E2E communication includes and/orcorresponds to a QoS flow. Some wireless networks configure a QoS flowwith operating rules, priority levels, classifications, and so forth,that influence how information is exchanged through the QoS flow. Forexample, a QoS profile indicates to a wireless network the QoSparameters and/or QoS characteristics of a particular QoS flow, such asa Guaranteed Flow Bit Rate (GFBR) parameter used to indicate an uplinkand/or downlink guaranteed bit rate for the QoS flow, a Maximum Flow BitRate (MFBR) parameters used to indicate a maximum uplink and/or downlinkbit rate for the QoS flow, an Allocation and Retention Priority (ARP)parameter that indicates a priority level, a pre-emption capability,and/or pre-emption vulnerability of the QoS flow, a Reflective QoSattribute (RQA) that indicates a type of traffic carried on the QoS flowis subject to Reflective QoS (e.g., implicit updates), a NotificationControl parameter that indicates whether notifications are requestedwhen a guaranteed flow bit rate cannot be guaranteed, or resumes, forthe QoS flow, an aggregate bit rate parameter that indicates an expectedaggregate bit rate for the collective non-guaranteed-bit-rate (Non-GBR)flows associated with a particular UE, default parameters for 5QI andARP priority levels, a Maximum Packet Loss Rate (MPLR) for uplink and/ordownlink that indicates a maximum rate for lost packets of the QoS flow,a Resource Type characteristic that indicates types of resources thatcan be used by the QoS flow (e.g., GBR resource type, Delay-critical GBRresource type, non-GBR resource type), a scheduling priority levelcharacteristic that distinguishes between multiple QoS flows of a sameUE, a Packet Delay Budget characteristic that provides an upper bound tohow long a packet may be delayed, a Packet Error Rate characteristicthat indicates an upper bound for a rate of PDUs unsuccessfullyreceived, an Averaging Window characteristics that indicates a window ofdata over which to calculate the GFBR and/or MFBR, a Maximum Data BurstVolume characteristic that indicates a largest amount of data that isrequired to be served over a pre-defined time period, and so forth. Insome implementations, the parameters and/or characteristics that specifythe configuration of a QoS flow can be pre-configured (e.g., default)and/or dynamically communicated, such as through the QoS profile. Thesevariations impact how the wireless network partitions the variouswireless network resources to support the QoS flow configuration.

For example, a UE can include three applications, where each applicationhas a different performance requirement (e.g., resource type, prioritylevel, packet delay budget, packet error rate, maximum data burstvolume, averaging window, security level). These different performancerequirements cause the wireless network to partition the wirelessnetwork resources assigned to the respective QoS flows assigned to eachapplication differently from one another.

To demonstrate, consider a scenario in which the UE includes a gamingapplication, an augmented reality application, and a social mediaapplication. In some instances, the gaming application interacts with aremote service (through the data network) to connect with another gamingapplication to exchange audio in real-time, video in real-time,commands, views, and so forth, such that the gaming application hasperformance requirements with high data volume and low latency. Theaugmented reality application also interacts with a remote servicethrough the data network to transmit location information andsubsequently receive image data that overlays on top of a camera imagegenerated at the UE. Relative to the gaming application, the augmentedreality application utilizes less data, but has some time-sensitivity tomaintain synchronization between a current location and a correspondingimage overlay. Finally, the social media application interacts with aremote service through the data network to receive feed information,where the feed information has less data volume and time-criticalityrelative to data consumed by the augmented reality application and/orthe gaming application.

Based upon these performance requirements, the wireless networkestablishes QoS flows between the applications and a data network, wherethe wireless network constructs each QoS flow based on QoS requirements,QoS parameters and/or QoS characteristics (e.g., resource type, prioritylevel, packet delay budget, packet error rate, maximum data burstvolume, averaging window, security level) that indicate a high datavolume performance requirement and a time-sensitivity performancerequirement. In implementations, the QoS requirements, the QoSparameters and/or the QoS characteristics included in a QoS profilecorrespond to the performance requirements of the QoS flow. As oneexample, the wireless network processes a QoS profile associated with afirst QoS flow that configures any combination of a GFBR parameter, aMaximum Data Burst Volume characteristic, an ARP parameter, and soforth. The wireless network then constructs the QoS flow by partitioningthe wireless network resources based on the QoS parameters and/orcharacteristics.

While the configurability of the QoS flows provide flexibility to thewireless network to dynamically modify how the wireless networkresources are allocated, the configurability adds complexity in how thewireless network processes information that is exchanged between theendpoints. Some implementations train DNNs to perform some or all of thecomplex processing associated with exchanging information using E2Ecommunications with various configurations. By training a DNN on thediffering processing chain operations and/or wireless network resourcepartitioning, the DNN can replace the conventional complex functionalityas further described. The usage of DNNs in an E2E communication alsoallows a network entity to adapt the DNN to changing operatingconditions, such as by modifying various parameter configurations (e.g.,coefficients, layer connections, kernel sizes).

One or more implementations determine an E2E ML configuration forprocessing information exchanged through an E2E communication. In somecases, an end-to-end machine-learning controller (E2E ML controller)obtains capabilities of device(s) associated with end-to-endcommunications in a wireless network, such as machine-learning (ML)capabilities of device(s) participating in the E2E communication, anddetermines an E2E ML configuration based on the ML capabilities (e.g.,supported ML architectures, supported number of layers, availableprocessing power, memory limitation, available power budget, fixed-pointprocessing vs. floating point processing, maximum kernel sizecapability, computation capability) of the device(s). Alternately oradditionally, the E2E ML controller identifies a current operatingenvironment and determines the E2E ML configuration based on the currentoperating environment. Some implementations of the E2E ML controllercommunicate with a network-slice manager to determine an E2E MLconfiguration that corresponds to a network slice (e.g., a partitioningof wireless network resources). In determining the E2E ML configuration,some implementations of the E2E ML controller partition the E2E MLconfiguration based on the device(s) participating in the E2Ecommunication and communicate a respective partition of the E2E MLconfiguration to each respective device.

To demonstrate, consider FIG. 10 that illustrates an example environment1000 in which example E2E ML configurations for E2E communicationsinclude DNNs operating at multiple devices. In implementations, theexample environment 1000 illustrates aspects of machine-learningarchitectures for broadcast and multicast communications. Theenvironment 1000 includes the UE 110, the base station 120, and theremote service 170 of FIG. 1 , and the core network server 302 of FIG. 3. In implementations, the UE 110 and the remote service 170 exchangeinformation with one another using E2E communication 1002 and E2Ecommunication 1004. For clarity, the E2E communications 1002 and 1004are illustrated as being separate and single-directional E2Ecommunications such that the exchange of information over eachcommunication E2E corresponds to one direction (e.g., E2E communication1002 includes uplink transmissions, E2E communication 1004 includesdownlink transmissions), but it is to be appreciated that in alternateor additional implementations, the E2E communication 1002 and the E2Ecommunication 1004 correspond to a single, bi-directional E2Ecommunication that includes both, signified in the environment 1000 witha dashed line 1006. In implementations, the E2E communication 1002 andthe E2E communication 1004 (in combination) correspond to a single QoSflow.

The environment 1000 also includes the E2E ML controller 318 that isimplemented by the core network server 302, where the E2E ML controller318 determines an E2E ML configuration for the E2E communication 1002and/or the E2E communication 1004. In some implementations, the E2E MLcontroller determines a first E2E ML configuration for the E2Ecommunication 1002 and a second E2E ML configuration for the E2Ecommunication 1004, such as when each E2E communication corresponds tosingle-directional information exchanges. In other implementations, theE2E ML controller determines an E2E ML configuration for abi-directional E2E communication that includes both E2E communications1002 and 1004. For example, in response to the UE 110 requesting aconnection to the remote server 170, such as through the invocation ofan application, the E2E ML controller determines an E2E ML configurationfor a corresponding connection based on any combination of MLcapabilities of the UE 110 (e.g., supported ML architectures, supportednumber of layers, processing power available for ML processing, memoryconstraints applied to ML processing, power budget available for MLprocessing, fixed-point processing vs. floating point processing),performance requirements associated with the requested connection (e.g.,resource type, priority level, packet delay budget, packet error rate,maximum data burst volume, averaging window, security level), availablewireless network resources, ML capabilities of intermediary devices(e.g., the base station 120, the core network server 302), a currentoperating environment (e.g., channel conditions, UE location), and soforth. As one example, with reference to FIGS. 9-1, 9-2 , and FIG. 10 ,the E2E ML controller 318, by way of the core network server 302,receives base station metrics and/or UE metrics that describe thecurrent operating environment. As another example, the E2E MLcontroller, by way of the core network server 302, receives base stationML capabilities and/or UE capabilities. Alternately or additionally, theE2E ML controller communicates with the network-slice manager 320 toidentify a network slice that partitions the wireless network resourcesin a manner that supports QoS requirement(s).

In one or more implementations, the E2E ML controller 318 analyzes aneural network table based upon any combination of the devicecapabilities, the wireless network resource partitioning, the operatingparameters, the current operating environment, the ML capabilities, andso forth, to determine the E2E ML configuration. While described asbeing implemented by the core network server 302, in alternate oradditional implementations, the E2E ML controller 318 may be implementedby another network entity, such as the base station 120.

To illustrate, and with reference to FIG. 8 , the training module 270and/or the training module 314 train the machine-learning module 400with variations of the training data 802 that reflect differentcombinations of the capabilities, wireless network resourcepartitioning, the operating parameters, the current operatingenvironment, and so forth. The training module extracts and stores thearchitecture and/or parameter configurations (e.g., architecture and/orparameter configurations 808) in a neural network table such that, at alater point in time, the E2E ML controller 318 accesses the neuralnetwork table to obtain and/or identify neural network formationconfigurations that correspond to a determined E2E ML configuration. TheE2E ML controller 318 then communicates the neural network formationconfigurations to various devices and directs the respective device toform a respective DNN as further described.

In determining the E2E ML configuration, the E2E ML controller 318sometimes partitions the E2E ML configuration based on devicesparticipating in the corresponding E2E communication. For example, theE2E ML controller 318 determines a first partition of the E2E MLconfiguration that corresponds to processing information at the UE 110,a second partition of the E2E ML configuration that corresponds toprocessing information at the base station 120, and a third partition ofthe E2E ML configuration that corresponds to processing information atthe core network server 302, where determining the partitions can bebased on any combination of the capabilities, wireless network resourcepartitioning, the operating parameters, the current operatingenvironment, and so forth.

As one example, consider an E2E communication that corresponds to voicetransmissions over a wireless network, such as the E2E communication1002, the E2E communication 1004, and/or a combination of both E2Ecommunications. In determining an E2E ML configuration for the E2Ecommunication, the E2E ML controller 318 alternately or additionallyidentifies that performance requirement(s) of the E2E communicationindicates large volumes of data transfer with low latency requirements.Based on the performance requirement(s), the E2E ML controlleridentifies an E2E ML configuration that, when formed by the respectiveDNN(s), performs end-to-end functionality that exchanges voicecommunications and satisfies the performance requirement(s). Toillustrate, the E2E ML controller determines an E2E ML configurationthat performs end-to-end functionality for transmitting voice from a UEto a core network server, such as signal processing, voice encoding,channel encoding, and/or channel modulation at the UE side, channeldecoding, demodulation, and/or signal processing at the base stationside, decoding voice at the core network server side, and so forth, andselects a configuration designed to satisfy the performancerequirements.

Some implementations partition an E2E ML configuration based the MLcapabilities of devices participating in the E2E communication and/orthe performance requirements. A UE, for instance, may have lessprocessing resources (e.g., processing capabilities, memory constraints,quantization constraints, fixed-point vs. floating point computations,FLOPS, power availability relative to a base station and/or a corenetwork server, which can be indicated through the ML capabilities. Inresponse to identifying the different processing resources through ananalysis of the ML capabilities, the E2E ML controller partitions theE2E ML configuration such that a first partition (e.g., at the UE 110)forms a DNN that performs less processing than a DNN formed by a secondor third partition (e.g., at the base station, at the core networkserver). Alternately or additionally, the E2E ML controller partitionsthe E2E ML configuration to produce neural networks designed to notexceed device capabilities. For example, based on analyzing thecapabilities, the E2E ML controller directs the UE to form a DNN withless layers and a smaller kernel size relative to a DNN formed by thebase station and/or the core network server based on processingconstraints of the UE. Alternately or additionally, the E2E MLcontroller partitions the E2E ML configuration to form, at the UE) aneural network with an architecture (e.g., a convolutional neuralnetwork, a long short-term memory (LSTM) network, partially connected,fully connected) that processes information without exceeding memoryconstraints of the UE. In some instances, the E2E ML controllercalculates whether an amount of computation performed at each devicecollectively meets a performance requirement corresponding to a latencybudget and determines an E2E ML configuration designed to meet theperformance requirement.

In the environment 1000, the E2E ML controller 318 determines a firstE2E ML configuration for processing information exchanged through theE2E communication 1002 and determines to partition the first E2E MLconfiguration across multiple devices such as by partitioning the firstE2E ML configuration across the UE 110, the base station 120, and thecore network server 302 based on device capabilities. In other words,some implementations determine an E2E ML configuration that correspondsto a distributed DNN in which multiple devices implement and/or formportions of the DNN. To communicate the partitioning, the E2E MLcontroller 318 identifies a first neural network formation configuration(NN formation configuration) that corresponds to a first partition ofthe E2E ML configuration and communicates, by using the core networkserver 302, the first NN formation configuration to the UE 110. The E2EML controller 318 and/or the core network server 302 then directs the UEto form a user equipment-side deep neural network 1008 (UE-side DNN1008) for processing information exchanged through the E2E communication1002. Similarly, the E2E ML controller 318 identifies a second NNformation configuration that corresponds to a second partition of theE2E ML configuration and communicates the second NN formationconfiguration to the base station 120. The E2E ML controller 318 and/orthe core network server 302 then directs the base station 120 to form,using the second NN formation configuration, a base station-side deepneural network 1010 (B S-side DNN 1010) for processing informationexchanged through the E2E communication 1002. The E2E ML controller 318also identifies and communicates a third NN formation configuration tothe core network server 302 to use in forming a core network server-sidedeep neural network 1012 (CNS-side DNN 1012) for processing informationexchanged through the E2E communication 1002.

In implementations, the E2E ML controller 318 partitions the E2E MLconfiguration to distribute processing computations performed over theE2E communication such that the UE-side DNN 1008 performs lessprocessing relative to the BS-side DNN 1010 (e.g., a UE-side DNN 1008that uses less layers, less data processing points, and so forth,relative to the BS-side DNN 1010). Alternately or additionally, the E2EML controller 318 partitions the E2E ML configuration such that theBS-side DNN 1010 performs less processing relative to CNS-side DNN 1012.In combination, the processing performed by the UE-side DNN 1008, theBS-side DNN 1010, and the CNS-side DNN 1012 exchange information acrossthe E2E communication 1002.

In a similar manner, the E2E ML controller 318 determines a second E2EML configuration for processing information exchanged through the E2Ecommunication 1004, where the E2E ML controller partitions and/ordistributes the second E2E ML configuration across multiple devices. Inthe environment 1000, this partitioning corresponds to a core networkserver-side deep neural network 1014 (CNS-side DNN 1014), a basestation-side deep neural network 1016 (BS-side DNN 1016), and a userequipment-side deep neural network 1018 (UE-side DNN 1018). Incombination, the processing performed by the CNS-side DNN 1014, theBS-side DNN 1016, and the UE-side DNN 1018 corresponds to exchanginginformation using the E2E communication 1004. While the E2E MLcontroller determines the first and second E2E ML configurationsseparately in the environment 1000 for single-directional E2Ecommunications (e.g., the E2E communications 1002 and 1004), it is to beappreciated that in alternate or additional implementations, the E2E MLcontroller 318 determines a single E2E ML configuration that correspondsto exchanging bi-directional information using an E2E communication.Accordingly, with respect to the E2E communication 1002 and/or the E2Ecommunication 1004, the E2E ML controller 318 determines a partitionedE2E ML configuration and communicates respective portions of thepartitioned E2E ML configuration to the devices participating in the E2Ecommunication 1002 and/or the E2E communication 1004.

In implementations, the E2E ML controller 318 periodically reassessmetrics, performance requirements, wireless link performance, processingcapabilities of devices or other aspects affecting, or providing anindication of, a current operating environment and/or a currentperformance (e.g., bit errors, BLER) to determine whether to update theE2E ML configuration. For example, the E2E ML controller 318 determinesmodifications (e.g., parameter changes) to an existing DNN to betteraccommodate the performance requirements of devices, applications,and/or transmissions in a wireless network. A UE changing location mayimpact on the wireless link performance, or a user opening anapplication at the UE may reduce the processing capability the userequipment can provide for machine learning. By reassessing dynamicallychanging conditions (e.g., changes in the operating environment, changesin the devices), the E2E ML controller can modify or update the E2E MLconfiguration to improve an overall efficiency of how the wirelessnetwork resources are utilized.

Having described E2E ML for wireless networks, consider now a discussionof machine-learning architectures for broadcast and multicastcommunications that are in accordance with one or more implementations.

Machine-Learning Architectures for Broadcast and MulticastCommunications

Wireless communications systems use broadcast and multicastcommunications to propagate information from one device to multipledevices. In propagating the information, the wireless communicationsystem can replicate, copy, or share the same information amongst themultiple devices, such as by transmitting replications of theinformation on multiple beams or enabling multiple devices to access theinformation from the same transmission. As one example, a base stationtransmits information on broadcast channel(s) to provide multiplereceiving devices with access to a corresponding network. As anotherexample, the base station can broadcast information associated with aservice to multiple devices, such as a traffic broadcast service thatdelivers traffic updates to multiple vehicles.

Oftentimes, any capable receiving device within working range of a basestation has access to the information included in the broadcastcommunications. However, where broadcast communications disseminateinformation in a one-to-all manner, multicast communications selectivelypropagate the information in a one-to-many manner. Instead of providinginformation to any capable receiving device, multicast communicationstarget specific devices (e.g., a subset of the possible receivingdevices).

Transmitting information to multiple devices, whether broadcast ormulticast communications, poses several challenges. As one example,transmitting information to multiple devices creates a more complextransmission environment relative to transmitting information to asingle device. To illustrate, a mobile device in communication with abase station changes the transmission environment by moving from a firstlocation to a second location. This becomes further compounded whenmultiple mobile devices receiving broadcast or multicast communicationsfrom the base station move in varying directions from one another. Asanother example, the multiple devices oftentimes have differentprocessing capabilities from one another. For example, a first devicereceiving the broadcast or multicast communications may have lessprocessing power than a second device receiving the broadcast ormulticast communications. The different processing capabilities amongdifferent UEs can impact how the base station transmits the information.These differences of device capabilities, as well as changing channelconditions, oftentimes leads to inefficient use of the networkresources.

Various implementations utilize machine-learning architectures forbroadcast and multicast communications. In implementations, a networkentity determines a configuration of a deep neural network (DNN) forprocessing broadcast or multicast communications transmitted over awireless communication system, where the communications are directed toa targeted group of user equipments (UEs). The network entity forms anetwork-entity DNN based on the determined configuration of the DNN andprocesses the broadcast or multicast communications using thenetwork-entity DNN. In implementations, the network entity forms acommon DNN to process and/or propagate the broadcast or multicastcommunications to the targeted group of UEs.

FIG. 11 illustrates an example environment 1100 that utilizesmachine-learning architectures for broadcast and multicastcommunications in accordance with one or more implementations. Theenvironment 1100 includes the base station 120 of FIG. 1 , and multipleUEs, labeled UE 1102, UE 1104, UE 1106, and UE 1108, respectively. Invarious implementations, the UEs 1102, 1104, 1106, and 1108 representinstances of the UE 110 of FIG. 1 . Collectively, the UEs 1102, 1104,1106, and 1108 form a targeted group of UEs 1110 for broadcastcommunications from the base station 120, where the targeted groupincludes multiple UEs (e.g., at least two UEs).

The base station 120 includes a base-station side deep neural network1112 (BS-side DNN 1112) that processes broadcast communications 1114 fortransmission over a wireless communication system. For instance, withreference to FIGS. 5 and 6 , the BS-side DNN 1112 performs transmitterchain operations to generate and/or propagate the broadcastcommunications 1114 over a wireless interface to the targeted group ofUEs 1110 (e.g., UEs 1102, 1104, 1106, and 1108). Alternately oradditionally, with reference to FIG. 10 , the BS-side DNN 1112represents a DNN formed from a portion of a partitioned E2E MLconfiguration. Similarly, each UE includes a user-equipment side deepneural network (UE-side DNN), labeled as UE-side DNN 1116, UE-side DNN1118, UE-side DNN 1120, and UE-side DNN 1122, where the UE-side DNNsperform receiver chain operations. In some implementations, the UE-sideDNNs represent device-specific DNNs and/or DNNs based on a partitionedE2E ML configuration. In implementations, a neural network manager, anE2E ML controller and/or a network-slice manager determines aconfiguration for the BS-side DNN 1112 and/or the UE-side DNNs 1116,1118, 1120, and 1122 as further described.

The BS-side DNN 1112 processes broadcast communications 1114 to transmitinformation over the wireless communication system, where the processingcan include pre-transmission operations. The BS-side DNN 1112 caninclude any combination of ML-architectures, such as a convolutionalneural network (CNN) architecture, a recurrent neural network (RNN)architecture, a LSTM architecture, fully-connected layers architecture,or partially-connected layers architectures. At times, the base station120 uses a common DNN architecture to form a common DNN (e.g., theBS-side DNN 1112) for communicating with each targeted UE, such as allcapable UEs in a cell coverage area associated with the base station120. Accordingly, each respective UE-side DNN (e.g., UE-side DNNs 1116,1118, 1120, and 1122) performs complementary processing to the commonDNN. In other words, in some implementations, the UE-side DNNs 1116,1118, 1120, and 1122 receive and process the broadcast communications1114 using similar DNN architectures. However, in alternateimplementations, the UE-side DNNs may receive and process the broadcastcommunication using different DNN architectures from one another, suchas by each respective UE using a respective DNN architecture based uponthe respective processing abilities of the UE. In some implementations,a core network server alternately or additionally processes thebroadcast communications (not illustrated here) using acore-network-server-side deep neural network (CNS-side DNN), such asthat described with reference to FIG. 10 .

FIG. 12 illustrates an example environment 1200 that utilizesmachine-learning architectures for broadcast and multicastcommunications in accordance with one or more implementations. Theenvironment 1200 includes the base station 120 of FIG. 1 , and the UEs1102, 1104, 1106, and 1108 of FIG. 11 that are all with the base station120 coverage area. In the environment 1200, the base station 120includes a base-station side deep neural network 1202 (BS-side DNN 1202)configured to transmit multicast communications 1204 to a targeted groupof UEs 1206 that includes two UEs: the UE 1104 and the UE 1108. Forinstance, with reference to FIGS. 5 and 6 , the BS-side DNN 1202performs transmitter chain operations to process and/or generate themulticast communications 1204. At times, with reference to FIG. 10 , thebase station 120 forms BS-side DNN 1202 based on a portion of apartitioned E2E ML configuration as further described. In theenvironment 1200, the targeted group of UEs 1206 corresponds to a subsetof UEs that are included in a coverage area associated with the basestation 120.

Each UE in the targeted group of UEs 1206 (e.g., UE 1104, UE 1108)includes a respective UE-side DNN that processes and recoversinformation from the multicast communications 1204. To illustrated, theUE 1104 includes a UE-side DNN 1208 and the UE 1108 includes a UE-sideDNN 1210, where the UE-side DNNs perform receiver chain operationsand/or complementary operations to the BS-side DNN 1202 to recover themulticast information. In some implementations, the UEs 1104 and 1108form the UE-side DNNs 1208 and 1210 based on a portion of a partitionedE2E ML configuration, such as the partitioned E2DE ML configuration usedto form the BS-side DNN 1202. While not illustrated in FIG. 12 , someimplementations include a CNS-side DNN at a core network server toprocess the multicast communications as further described with referenceto FIG. 10 .

By using DNNs to process broadcast and/or multicast communications, awireless communication system can adapt the processing to improve thecommunication exchanges in the system. As one example, the wirelesscommunication system configures the DNNs (e.g., the BS-side DNN, theUE-side DNNs, a CNS-side DNN) based on a current operating condition ordiverse UE capabilities to improve an overall performance of thebroadcast and/or multicast communications exchanged between the devices(e.g., lower bit errors, improved signal quality, improved latency).Alternately or additionally, the wireless communication systemconfigures the DNNs based on a lowest-common configuration that each UEin a targeted group of UEs supports (e.g., using a configuration that aUE with the lowest processing power can support).

In implementations, a network entity (e.g., the core network server 302,the base station 120) uses any combination of a neural network manager,an E2E ML controller, and/or a network slicer, to determine one or moreML configuration(s) that process broadcast or multicast communications.The ML configuration(s) can be based on content requirements of thebroadcast and/or multicast communications, a current operatingenvironment, changes in the current operating environment, UEcapabilities, UE characteristics, etc. For instance, the contentrequirements specify a quality requirement, a resolution requirement, ora frames-per-second requirement for a particular UE in a targeted groupof UEs receiving the broadcast and/or multicast communications.Alternately or additionally, the content requirements specify a qualityrequirement, a resolution requirement, or a frames-per-secondrequirement for each UE in the targeted group of UEs. The network entityanalyzes these content requirements and determines device-specific MLconfiguration(s) or an E2E ML configuration designed to fulfill thecontent requirements for each UE and/or the particular UE, such asdescribed with reference to FIG. 10 . At times, the network entitydetermines to change the ML configuration when the content requirementsof the broadcast or multicast communication requirements change.Alternately or additionally, the network entity determines the MLconfiguration(s) based on a network slice designed to meet performanceand/or content requirements. In some implementations, the networkentity, determines an ML configuration for each of the devices based onUE characteristics, such as an estimated location of each device,capabilities of each device, UE velocity, and so forth.

Signaling and Control Transactions to Configure Deep Neural Networks

FIGS. 13, 14, 15, and 16 illustrate example signaling and controltransaction diagrams between a base station, a user equipment, and/or acore network server in accordance with one or more aspects of usingmachine-learning architectures for broadcast and multicastcommunications in wireless communications. The signaling and controltransactions may be performed by the base station 120 and the UE 110 ofFIG. 1 , or the core network server 302 of FIG. 3 , using elements ofFIGS. 1-12 .

A first example of signaling and control transactions of usingmachine-learning architectures for broadcast and multicastcommunications in wireless communications is illustrated by thesignaling and control transaction diagram 1300 of FIG. 13 . At 1305, thebase station 120 optionally receives metrics and/or UE capabilities fromthe UE(s) 110, where the UE(s) in FIG. 13 correspond to a targeted groupof UEs, such as the targeted group of UEs 1110 as illustrated in FIG. 11or the targeted group of UEs 1206 as illustrated in FIG. 12 . Forinstance, the base station 120 receives UE capabilities from one or moreUEs in the targeted group of UEs in response to sending a request for UEcapabilities. In some implementations, the UE capabilities includeML-related capabilities, such as a maximum kernel size capability, amemory limitation, a computation capability, supported ML architectures,supported number of layers, available processing power, memorylimitation, available power budget, and fixed-point processing versusfloating point processing. As another example, the base station 120receives UE metrics from one or more UE of the UE(s) 110, such as powermeasurements (e.g., RSS), error metrics, timing metrics, QoS, latency, aReference Signal Receive Power (RSRP), SINR information, CQI, CSI,Doppler feedback, QoS, latency, etc. Alternately or additionally, thebase station 120 generates BS metrics based on communications with theUE(s), such as those described with reference to FIG. 8 .

At 1310, the base station 120 determines to transmit broadcast ormulticast communications. For instance, the base station 120 determinesto transmit machine-type communications (MTC) to a subset of IoT devicesusing multicast communications. As another example, the base station 120determines to transmit a paging message to all devices in a cellcoverage area using broadcast communications.

In response to determining to transmit the broadcast or multicastcommunications, the base station 120 (by way of the BS neural networkmanager 268, an E2E ML controller implemented by the base station,and/or a network-slice manager implemented by the base station)determines a configuration of a DNN for processing the communications at1315. The configuration of the DNN can include a (partitioned) E2E MLconfiguration and/or multiple DNN configurations for multiple devices.In some implementations, the base station neural network manager 268determines to use a default configuration for the DNN. As anotherexample, the base station neural network manager 268 determines theconfiguration by communicating with an E2E ML controller and/or anetwork-slice manager, such as the E2E ML controller 318 and/or thenetwork-slice manager 320 at the core network server 302 or an E2E MLcontroller and/or network-slice manager implemented at the base station120. To illustrate, the base station neural network manager 268communicates content and/or performance requirements to the E2E MLcontroller and/or a network-slice manager, such as quality requirements,resolution requirements, frames per second requirements, latencyrequirements, or bandwidth requirements, to determine a configurationfor the DNN directed to meet the requirements.

In some implementations, the base station 120 determines DNNconfiguration(s) based on one or more (estimated) locations of UEs, suchas estimated locations of each UE in a targeted group of UEs, a singleestimated location of a single UE in the targeted group of UEs, or therespective estimated locations of a subset of UEs in the targeted groupof UEs. The targeted group of UEs can include all UEs within a cellcoverage area or a subset of UEs within the cell coverage area, where,in various implementations, a targeted group of UEs includes at leasttwo UEs. For example, in response to identifying that at least one UE ofthe targeted UEs is located at an edge of the cell coverage area, thebase station 120, by way of the any combination of the base stationneural network manager 268, a network-slice manager and/or an E2E MLcontroller, determines DNN configuration(s) to reliably transmit thebroadcast or multicast communications to the UE located at the edge ofthe cell coverage, in addition to the other UEs in the targeted group ofUEs. In some implementations, this includes analyzing a neural networktable based upon the estimated location(s), UE capabilities, wirelessnetwork resource partitioning, operating parameters, the metricsreceived at 1305, a current operating environment, UE ML capabilities,and so forth, to determine the configuration of the DNN.

In determining the configuration of the DNN, the base station 120sometimes determines gradient or scaled versions of the configurationand/or ML architecture. Consider, for example, a scenario in which afirst UE in the targeted group of UEs has less processing power relativeto a second UE in the targeted group of UEs. Some implementationsidentify simplified versions of the determined configuration andcommunicate the simplified version to the first UE with less processingpower. A simplified version of the determined configuration denotes ascaled version of the ML configuration with less complexity relative tothe determined configuration, such as a reduced number of layerconnections, less processing points, etc. More broadly, gradientversions of an ML or DNN configuration are variations of an architectureor configuration that perform different levels of processing, such as byvariations with different layer connections to reduce or increase thenumber of processing nodes, different filtering to reduce or increase anumber of data points processed, and so forth. Accordingly, indetermining the configuration of the DNN, the base station 120 candetermine gradient or scaled versions of the configuration based upon UEcapabilities.

At 1320, the base station 120 optionally communicates the configurationof the DNN to the UE(s) 110 at 1325. As one example, when the basestation determines to use a default DNN configuration, the UE 110implicitly determines to use a default DNN configuration in response tonot receiving a DNN configuration from the base station. Thus, the basestation does not communicate the DNN configuration explicitly. Asanother example, the base station 120 communicates a partition of an E2EML configuration or a UE-specific DNN configuration to the UE 110. Theconfiguration of the DNN can be included in any suitable manner, such asthrough the use of a broadcast or unicast message.

At 1330, the base station 120 forms a DNN based on the configuration ofthe DNN determined at 1315. In implementations, the DNN formed by thebase station (e.g., BS-side DNNs 1112) performs at least some processingfor transmitting broadcast or multicast communications over a wirelesscommunication system. Similarly, at 1335, the UE 110 forms a DNN basedon the configuration determined at 1315. For instance, the UE 110accesses a neural network table to obtain one or more parameters anduses the parameters to form the DNN as described with reference to FIG.8 . In implementations, the UE forms the DNN using a default neuralnetwork formation configuration, a portion of an E2E ML configuration,or a UE-specific neural network formation configuration, where the DNNformed by the UE 110 (e.g., UE-side DNNs 1116, 1118, 1120, and 1122)performs at least some processing related to consuming the broadcast ormulticast communications.

Afterwards, at 1340, the base station 120 and the UE 110 processbroadcast or multicast communications using the DNNs, such as thatdescribed with reference to FIGS. 11 and 12 . In implementations, thebase station 120 and/or the UE 110 iteratively perform the signaling andcontrol transactions described in the signaling and control transactiondiagram 1300, signified by dashed line 1345. These iterations allow thebase station 120 and/or the UE 110 to dynamically modify the DNNsprocessing the broadcast or multicast communications based upon changingoperating conditions as further described.

A second example of signaling and control transactions of usingmachine-learning architectures for broadcast and multicastcommunications in wireless communications is illustrated by thesignaling and control transaction diagram 1400 of FIG. 14 . The examplediagram 1400 performs similar transactions to those described withrespect to 1300, where the signaling and control transactions includeinteractions with the core network server 302.

At 1405, the core network server 302 optionally receives metrics and/orcapabilities from base station 120 and/or the UE(s) 110 (by way of thebase station 120), where the UE(s) in FIG. 14 correspond to a targetedgroup of UEs, such as the targeted group of UEs 1110 as illustrated inFIG. 11 or the targeted group of UEs 1206 as illustrated in FIG. 12 . Inimplementations, the core network server 302 receives UE capabilities orUE metrics from one or more UEs in the targeted group of UEs through thebase station 120, such as ML-related capabilities or processingcapabilities. Alternately or additionally, the core network server 302receives BS metrics from the base station 120 that are based oncommunications that the base station exchanges with the UE(s) asdescribed with reference to FIG. 8 . Alternately or additionally, thecore network server 302 receives BS capabilities from the base station120, such as processing power, power state, capacity (e.g., supportablenumber of connections), working range, and so forth.

At 1410, the core network server 302 determines to transmit broadcast ormulticast communications and determines a configuration of a DNN forprocessing the communications at 1415. In implementations, the corenetwork neural network manager 312 determines the configuration bycommunicating with the E2E ML controller 318 and/or the network-slicemanager 320. The configuration can, at times, be based on anycombination of content requirements, performance requirements, UEcapabilities, UE characteristics, or a current operating condition.

In some implementations, the core network server 302 determines theconfiguration of the DNN based on one or more (estimated) locations ofUEs, such as estimated locations of a targeted group of UEs that includeall UEs within a cell coverage area or a subset of UEs within the cellcoverage area. To illustrate, the core network server 302 analyzes aneural network table based upon any combination of the estimatedlocation(s), to determine the configuration. Alternately oradditionally, the core network server 302 analyzes the neural networktable based on UE capabilities, wireless network resource partitioning,content requirements, performance requirements, the metrics received at1305, a current operating environment, UE ML capabilities, one or moreUE velocities, and so forth, to determine the configuration of the DNN.Any type of configuration can be determined for the DNN, such as aCNS-specific DNN configuration, a BS-side DNN configuration, s UE-sideDNN configurations, and/or partitioned E2E ML configurations as furtherdescribed.

In determining the configuration of the DNN, the core network server 302sometimes determines simplified versions of the configuration and/or MLarchitecture, where a simplified version of the determined configurationdenotes a scaled version of the ML configuration that has less or moreprocessing complexity relative to the determined configuration, such asa reduced number of layer connections, less processing points, etc.Thus, as part of determining the configuration of the DNN, the corenetwork server 302 sometimes determines gradient or scaled versions ofthe configuration based upon UE capabilities.

At 1420, the core network server optionally communicates theconfiguration of the DNN to the base station 120 and/or the UE(s) 110 at1425. As one example, when the core network server 302 to use a defaultDNN configuration, the base station 120 and/or the UE 110 implicitlydetermine to use a default DNN configuration in response to notreceiving a DNN configuration from the core network server 302. Asanother example, the core network server 302 communicates partitions ofan E2E ML configuration or device-specific DNN configurations to thebase station 120 and/or the UE(s) 110.

At 1430, the core network server forms a core-network-server-side deepneural network (CNS-side DNN) based on the configuration of the DNNdetermined at 1415. In implementations, the DNN formed by the corenetwork server performs at least some processing for transmittingbroadcast or multicast communications over a wireless communicationsystem, which can include pre-transmission processing. At 1335, the basestation 120 forms a DNN based on the configuration of the DNN determinedat 1415. In implementations, the DNN formed by the base station performsat least some processing for transmitting broadcast or multicastcommunications over a wireless communication system, includingpre-transmission processing. Similarly, at 1440, the UE 110 forms a DNNbased on the configuration determined at 1415. For instance, the UE 110accesses a neural network table using information received at 1425 toobtain one or more parameters as described with reference to FIG. 8 .This can include forming a DNN using a default neural network formationconfiguration, a portion of an E2E ML configuration, or a UE-specificneural network formation configuration. In implementations, the DNNformed by the UE 110 performs at least some processing for transmittingbroadcast or multicast communications over a wireless communicationsystem.

Afterwards, at 1445, the core network server 302, the base station 120and the UE(s) 110 process broadcast or multicast communications usingthe DNNs, such as that described with reference to FIGS. 11 and 12 . Inimplementations, the core network server 302, the base station 120and/or the UE 110 iteratively perform the signaling and controltransactions described in the signaling and control transaction diagram1400, signified with dashed line 1450. These iterations allow the corenetwork server 302, the base station 120 and/or the UE 110 todynamically modify the DNNs processing the broadcast or multicastcommunications based upon changing operating conditions as furtherdescribed.

Changing operating conditions impact the performance of how well eachDNN processes information. To illustrate, and with reference to FIG. 8 ,the training module 270 and/or the training module 314 generate neuralnetwork formation configurations based on operating conditions asdescribed by the input characteristics. This includes operatingconditions corresponding to broadcast and multicast communications, suchas operating conditions that include estimated UE locations, number oftargeted UEs, channel conditions, content requirements, or network sliceconfigurations. As current operating conditions deviate from theoperating conditions used to train a DNN, the performance of the DNNbegins to deteriorate.

Various implementations measure a performance of DNN(s) used to exchangebroadcast or multicast communications and determine to makemodifications to the DNNs when the performance fails to meet a thresholdvalue. This includes determining to make architectural changes and/orparameter changes to the DNN(s), such as by determining new neuralnetwork formation configurations that correspond to the changes. Themodifications to the DNN(s) can include small changes that involveupdating coefficient parameters of an existing architectureconfiguration, or larger changes that involve modifying an architectureconfiguration. A cost function, for example, measures a performance oferror within a system, such as through a comparison of a predicted valuegenerated by a DNN and an expected or true value. When the cost functionindicates that the performance of error within the system meets adesired threshold, various implementations determine to make smallmodifications to the system (e.g., parameter changes). When the costfunction indicates that the performance of error within the system failsto meet a desired threshold, various implementations determine to makelarge modifications to the system (e.g., architecture configurationchanges).

To demonstrate, consider now a third example of signaling and controltransactions of using machine-learning architectures for broadcast andmulticast communications in wireless communications, illustrated in FIG.15 by the signaling and control transaction diagram 1500. In someimplementations, the signaling and control transaction diagram 1500represents a continuation of the signaling and control transactiondiagram 1300 of FIG. 13 .

With reference to FIGS. 11 and 12 , the base station 120 and the UE(s)110 process broadcast or multicast communications using DNN(s) at 1505,where the UE(s) 110 represent a targeted group of UEs that can includeall the UEs in a cell coverage area or a subset of UEs in the cellcoverage area. In some implementations, the processing performed at 1505corresponds to the processing performed at 1340 of FIG. 13 .

At 1510, the base station 120 receives feedback from at least one UE inthe targeted group of UEs. For example, the UE 110 communicates one ormore metrics, such as BLER, SINR, CQI feedback, or a packet loss rate.Alternately or additionally, the base station 120 generates one or moremetrics, such as a Round-Trip Time (RTT) latency metric.

At 1515, the base station 120 analyzes the feedback. For example, thebase station 120 (by way of the BS neural network manager 268, an E2E MLcontroller implemented by the base station, and/or a network-slicemanager implemented by the base station) analyzes the feedback todetermine whether each UE in the targeted group of UEs meets aperformance threshold value and/or a cost function threshold value. Attimes, the UEs in the targeted group of UEs have varying performancethreshold values and/or cost function threshold values from one another.To illustrate, a first UE in the targeted group may have less processingpower relative to a second UE in the targeted group. Thus, different UEsmay have different performance requirements based on the relative UEcapabilities. Accordingly, the base station 120 compares metrics thatdescribe a performance of a UE-side DNN at the first UE to a differentperformance threshold value/cost function value than metrics thatdescribe a performance of a UE-side DNN at the second UE. For clarity,the above example discusses analyzing metrics that describe a UE-sideDNN performance, but it is to be appreciated that the metrics candescribe the performance of DNN chains (e.g., a CNS-side DNN, a BS-sideDNN, a UE-side DNN), partitioned DNNs(s) based on an E2E MLconfiguration, etc.

At 1520, the base station 120 (by way of the BS neural network manager268, an E2E ML controller implemented by the base station, and/or anetwork-slice manager implemented by the base station) determines amodification to the DNN(s) based on the feedback. In someimplementations, the base station 120 determines a large modificationthat changes an architecture configuration of the DNNs. For example, inresponse to determining that “X” number of UEs in the targeted groupfail to meet the respective performance and/or cost function thresholdvalue(s), the base station 120 determines a large modification thatcorresponds to changing an architecture configuration of one or moreDNN(s), where “X” represents an arbitrary number. Alternately oradditionally, the base station 120 determines a small modification thatcorresponds to changing parameter configurations without changing thearchitecture configuration, such as changing coefficient values,weights, or kernel sizes, when more than “X” UEs meet the performanceand/or cost function threshold value(s). The modification can correspondto DNNs formed from a partitioned E2E ML configuration and/or cancorrespond to device-specific DNNs (e.g., a CNS-side DNN, a BS-side DNN,a UE-side DNN). In various implementations, the base station 120determines gradient modifications based upon respective UE capabilitiesas further described.

At 1525, the base station 120 communicates the modification to the UE(s)110. In some implementations, the base station communicates identicalmodifications to each of the UEs in the targeted group of UEs. In otherimplementations, the base station communicates gradient modifications todifferent UEs based upon the respective UE capabilities (e.g., a UE withless processing power receives a simplified version of the modificationrelative to a UE with more processing power).

At 1530 the base station 120 updates a BS-side DNN based on themodification. Similarly, at 1535, the UE(s) 110, respectively, update aUE-side DNN based on the modification, where the BS-side DNN and theUE-side DNNs can correspond to device-specific DNNs or DNNs based on apartitioned E2E ML configuration. In implementations, the base station120 and/or the UE(s) 110 iteratively perform the signaling and controltransactions described in the signaling and control transaction diagram1500, signified with dashed line 1540. These iterations allow the basestation 120 and/or the UE(s) 110 to dynamically modify the DNNsprocessing the broadcast or multicast communications based upon changingoperating conditions as further described. A fourth example of signalingand control transactions of using machine-learning architectures forbroadcast and multicast communications in wireless communications isillustrated by the signaling and control transaction diagram 1600 ofFIG. 16 . The example diagram 1600 performs similar transactions tothose described with respect to 1500, where the transactions includeinteractions with the core network server 302. In some implementations,the signaling and control transaction diagram 1600 represents acontinuation of the signaling and control transaction diagram 1400 ofFIG. 14 .

At 1605, the core network server 302, base station 120 and the UE(s) 110process broadcast or multicast communications using DNN(s), where theUE(s) 110 represent a targeted group of UEs that can include all the UEsin a cell coverage area or a subset of UEs in the cell coverage area. Insome implementations, the processing performed at 1605 corresponds tothe processing performed at 1445 of FIG. 14 .

At 1610, the core network server 302 receives feedback from at least oneUE in the targeted group of UEs, by way of the base station 120. Forexample, the UE 110 communicates one or more metrics, such as BLER,SINR, CQI feedback, or a packet loss rate, as feedback to the corenetwork server 302. Alternately or additionally, the base station 120generates one or more metrics, such as a Round-Trip Time (RTT) latencymetric, and sends the metrics as feedback to the core network server302.

At 1615, the core network server 302 analyzes the feedback. For example,the core network server 302 (by way of the core network neural networkmanager 312, the E2E ML controller 318, and/or the network-slice manager320) analyzes the feedback to determine whether each UE in the targetedgroup of UEs meets a performance threshold value and/or a cost functionthreshold value. In some implementations, the core network server 302uses a same performance threshold value and/or cost function value whenanalyzing the performance of each UE in the targeted group of UEs. Othertimes, the core network server 302 uses varying performance and/or costfunction threshold values based on the relative UE capabilities of eachUE. Accordingly, the core network server 302 compares metrics thatdescribe a performance of a UE-side DNN at the first UE to a differentperformance threshold value/cost function value than metrics thatdescribe a performance of a UE-side DNN at the second UE. For clarity,the above example discusses analyzing metrics that describe a UE-sideDNN performance, but it is to be appreciated that the metrics candescribe the performance of DNN chains (e.g., a CNS-side DNN, a BS-sideDNN, a UE-side DNN), partitioned DNNs(s) based on an E2E MLconfiguration, etc.

At 1620, the core network server 302 (by way of the core network neuralnetwork manager 312, the E2E ML controller 318, and/or a network-slicemanager 320) determines a modification to the DNN(s) based on thefeedback. In some implementations, the core network server 302determines a large modification that changes an architectureconfiguration of the DNNs. Alternately or additionally, the core networkserver 302 determines a small modification that changes parameterconfigurations, such as coefficient values, weights, or kernel sizes.The modification can correspond to DNNs formed from a partitioned E2E MLconfiguration and/or device-specific DNNs (e.g., a CNS-side DNN, aBS-side DNN, a UE-side DNN). In various implementations, the corenetwork server 302 determines gradient versions of the modificationsbased upon respective UE capabilities as further described.

At 1625, the core network server 302 communicates the modification tothe base station 120 and/or the UE(s) 110 (by way of the base station120). In some implementations, the core network server 302 communicatesidentical modifications to each of the UEs in the targeted group of UEs.In other implementations, the core network server 302 communicatesgradient modifications to different UEs based upon the respective UEcapabilities (e.g., a UE with less processing power receives asimplified version of the modification relative to a UE with moreprocessing power).

At 1630, the core network server updates a CNS-side DNN based on themodification. Similarly, at 1635 and 1640, respectively, the basestation 120 updates a BS-side DNN based on the modification and theUE(s) 110 update respective UE-side DNNs based on the modification. TheCNS-side DNN, the BS-side DNN, and/or the UE-side DNNs can correspond todevice-specific DNNs or DNNs based on a partitioned E2E MLconfiguration. In implementations, the core network server 302, the basestation 120, and the UE(s) 110 iteratively perform the signaling andcontrol transactions described in the signaling and control transactiondiagram 1600, signified with dashed line 1645. These iterations allowthe core network server 302, the base station 120 and/or the UE(s) 110to dynamically modify the DNNs processing the broadcast or multicastcommunications based upon changing operating conditions as furtherdescribed.

Example Methods

Example methods 1700 and 1800 are described with reference to FIG. 17and FIG. 18 in accordance with one or more aspects of machine-learningarchitectures for broadcast and multicast communications. The order inwhich the method blocks are described are not intended to be construedas a limitation, and any number of the described method blocks can beskipped or combined in any order to implement a method or an alternatemethod. Generally, any of the components, modules, methods, andoperations described herein can be implemented using software, firmware,hardware (e.g., fixed logic circuitry), manual processing, or anycombination thereof. Some operations of the example methods may bedescribed in the general context of executable instructions stored oncomputer-readable storage memory that is local and/or remote to acomputer processing system, and implementations can include softwareapplications, programs, functions, and the like. Alternatively, oradditionally, any of the functionality described herein can beperformed, at least in part, by one or more hardware logic components,such as, and without limitation, Field-programmable Gate Arrays (FPGAs),Application-specific Integrated Circuits (ASICs), Application-specificStandard Products (ASSPs), System-on-a-chip systems (SoCs), ComplexProgrammable Logic Devices (CPLDs), and the like.

FIG. 17 illustrates an example method 1700 for using a machine-learningarchitecture for processing broadcast or multicast communications. Insome implementations, operations of method 1700 are performed by anetwork entity, such as any one of the base stations 120 or the corenetwork server 302.

At 1705, the network entity determines a configuration of a deep neuralnetwork (DNN) for processing broadcast or multicast communications thatare directed to a targeted group of UEs using a wireless communicationsystem, such as that described at 1315 of FIG. 13 or 1415 of FIG. 14 .In one or more implementations, the network entity (e.g., core networkserver 302) determines an E2E ML configuration for transmitting thebroadcast or multicast communications to the targeted group of UEs(e.g., targeted group of UEs 1110, targeted group of UEs 1206). Asanother example, the network entity (e.g., base station 120) determinesthe configuration of one or more DNNs (e.g., at 710, at 915), such as afirst configuration for a BS-side DNN and a second configuration for aUE-side DNN that performs complementary operations to one another. Thetargeted group of UEs can include all of the UEs in a cell coverage areaassociated with the network entity or a subset of UEs in the cellcoverage area.

In some implementations, the network entity determines to use a defaultconfiguration. In other implementations, the network entity determinesthe configuration based on one or more metrics (e.g., at 1305, at 1315,at 1405, at 1415). At times, the network entity determines gradientversions of the DNN configuration based on UE capabilities of one ormore UEs in the targeted group of UEs. Alternately or additionally, thenetwork entity determines the configuration for the DNN based on one ormore characteristics associated with the targeted group of UEs, such ascharacteristics that describe a current operating environment, anestimated location of each UE in the targeted group of UEs (e.g., atleast two estimated locations), UE ML capabilities, etc. In someimplementations, the network entity determines the configuration of theDNN based on one or more content requirements of the broadcast ormulticast communications (e.g., a quality requirement, a resolutionrequirement, a frames per second requirement).

At 1710, the network entity forms a network-entity DNN based on thedetermined configuration of the DNN, such as that described at 1330 ofFIG. 13 or 1430 of FIG. 14 . In implementations, the network entity(core network server 302) forms a CNS-side DNN, where the CNS-side DNNcan be based on a partition of an E2E ML configuration or adevice-specific DNN configuration. As another example, the networkentity (e.g., base station 120) forms the BS-side DNN 1202. Inimplementations, the network entity accesses a neural network table toobtain one or more architecture and/or parameter configurations. Inresponse to obtaining the configurations, the network entity forms thenetwork-entity DNN using the architecture and/or parameterconfigurations. In some implementations, the network entity optionallycommunicates the configuration of the DNN to the targeted group of UEs,such as that described at 1325 of FIG. 13 and at 1425 of FIG. 14 .Alternately or additionally, the network entity implicitly indicates, tothe targeted group of UEs, to use a default configuration for the DNN,such as by not communicating a configuration to the targeted group ofUEs.

At 1715, the network entity processes the broadcast or multicastcommunications using the network-entity DNN to direct the broadcast ormulticast communications to the targeted group of UEs using the wirelesscommunication system. For example, a CNS-side DNN (1012, 1014) at thecore network server 302 processes pre-transmission communications, suchas that described with reference to FIG. 6 . As another example, aBS-side DNN (1112, 1202) at the base station 120 processes the broadcastor multicast communications, including pre-transmission communications,to transmit the broadcast or multicast communications to the targetedgroup of UEs. In some implementations, the BS-side DNN corresponds to acommon DNN that transmits and directs the broadcast or multicastcommunications to each UE of the targeted group of UEs using a sameconfiguration rather than UE-specific DNNs.

FIG. 18 illustrates an example method 1800 for using a machine-learningarchitecture for processing broadcast or multicast communications. Insome implementations, operations of method 1800 are performed by anetwork entity, such as any one of the base stations 120 or the corenetwork server 302.

At 1805, the network entity processes broadcast or multicastcommunications using a deep neural network (DNN) to direct the broadcastor multicast communications to a targeted group of user equipments (UEs)using a wireless communication network. For example, the network entity(e.g. core network server 302, base station 120) processes the broadcastor multicast communications as described at 1340 of FIG. 13 , at 1445 ofFIG. 14 , at 1505 of FIG. 15 , and at 1605 of FIG. 16 . This can includeprocessing the broadcast or multicast communications using a DNN formedusing a partition of an E2E ML configuration or a device-specific DNN.In some implementations, the DNN performs transmitter chain operations,including pre-transmission processing, to direct transmissions to thetargeted group of UEs.

At 1810, the network entity receives feedback from at least one userequipment of the targeted group of UEs. For example, the network entity(e.g. core network server 302, base station 120) receives one or moremetrics from a UE in the targeted group of UEs as described at 1510 andat 1610 of FIGS. 15 and 16 . In implementations, the metricscharacterize a performance of one or more processing chains, such aspower measurements (e.g., RSS), error metrics, timing metrics, QoS,latency, a Reference Signal Receive Power (RSRP), SINR information, CQI,CSI, error metrics, or Doppler feedback. Alternately or additionally,the network entity receives information that characterizes a currentoperating environment, such as UE capabilities, a base station type(e.g., eNB, gNB, or ng-eNB), or a power mode. In some scenarios, thenetwork entity receives feedback related to the mobility of a UE, suchas location change information.

In response to receiving the feedback, the network entity determines amodification to the DNN based on the feedback at 1815. For example, thenetwork entity (e.g. core network server 302, base station 120) analyzesthe metrics as described at 1515 of FIG. 15 or at 1615 at FIG. 16 todetermine that “X” number of UEs in the targeted group of UEs meet aperformance and/or cost function threshold value. This can include thenetwork entity determining a large modification that changes anarchitecture configuration or a small modification that changesparameter configurations, such as that described at 1520 of FIG. 15 orat 1620 of FIG. 16 . As another example, the network entity determines amodification to the DNN based on a location change of a UE.

At 1820, the network entity transmits an indication of the modificationto the targeted group of UEs. The core network server 302, for example,transmits the indication to the targeted group of UEs by way of the basestation 120. In some implementations, the indication includes anindication of one or more entries in a neural network formationconfiguration, such as that described at 920 of FIG. 9 . At times, thenetwork entity (e.g., the core network server 302, the base station 120)transmits gradient versions of the modification. To illustrate, thenetwork entity transmits a first indication of a first neural networkformation configuration to a first UE of the targeted group of UEs and asecond indication of a second neural network formation configuration toa second UE, where the first and second neural network formationconfigurations are based on UE capabilities and form gradient versionsof a DNN architecture. For example, a second UE-side DNN formed usingthe second neural network formation configuration performs lessprocessing relative to a first UE-side DNN formed using the first neuralnetwork formation configuration. In other words, the DNN architecture ofthe second UE-side DNN is a gradient version of the DNN architecture ofthe first UE-side DNN.

At 1825, the network entity updates the DNN with the modification toform a modified DNN. For example, the core network server 302 updates aCNS-side DNN based on the modification, while the base station 120updates a BS-side DNN based on the modification. In response to updatingthe DNN, the network entity processes the broadcast or multicastcommunications using the modified DNN to direct the broadcast ormulticast communications to the targeted group of UEs at 1830.

Generally, any of the components, modules, methods, and operationsdescribed herein can be implemented using software, firmware, hardware(e.g., fixed logic circuitry), manual processing, or any combinationthereof. Some operations of the example methods may be described in thegeneral context of executable instructions stored on computer-readablestorage memory that is local and/or remote to a computer processingsystem, and implementations can include software applications, programs,functions, and the like. Alternatively or in addition, any of thefunctionality described herein can be performed, at least in part, byone or more hardware logic components, such as, and without limitation,Field-programmable Gate Arrays (FPGAs), Application-specific IntegratedCircuits (ASICs), Application-specific Standard Products (ASSPs),System-on-a-chip systems (SoCs), Complex Programmable Logic Devices(CPLDs), and the like.

Although techniques and devices for machine-learning architectures forbroadcast and multicast communications have been described in languagespecific to features and/or methods, it is to be understood that thesubject of the appended claims is not necessarily limited to thespecific features or methods described. Rather, the specific featuresand methods are disclosed as example implementations of machine-learningarchitectures for broadcast and multicast communications.

In the following, several examples are described.

Example 1

A method performed by a network entity associated with a wirelesscommunication system, the method comprising: determining a configurationof a deep neural network (DNN) for processing broadcast or multicastcommunications transmitted over the wireless communication system to atargeted group of user equipments (UEs); forming, at the network entity,a network-entity DNN based on the determined configuration of the DNN;and processing the broadcast or multicast communications using thenetwork-entity DNN to direct the broadcast or multicast communicationsto the targeted group of UEs using the wireless communication system.

Example 2

The method as recited in example 1, wherein the determining theconfiguration comprises: determining the configuration of the DNN based,at least in part, on at least one characteristic of the targeted groupof UEs.

Example 3

The method as recited in example 2, wherein the at least onecharacteristic comprises at least one of: an estimated location of atleast one user equipment (UE) in the targeted group of UEs; or at leastone UE capability of the at least one UE in the targeted group of UEs.

Example 4

The method as recited in and one of the examples 1 to 3, wherein thedetermining the configuration further comprises: determining a gradientversion of the configuration for at least one user equipment (UE) in thetargeted group of UEs based on processing capabilities of the at leastone UE.

Example 5

The method as recited in any one of the examples 1 to 4, whereinprocessing the broadcast or multicast communications using thenetwork-entity DNN further comprises: processing the broadcast ormulticast communications using a common DNN as the network-entity DNN todirect the broadcast or multicast communications to each UE of thetargeted group of UEs.

Example 6

The method as recited in any one of the examples 1 to 5, wherein thetargeted group of UEs is a subset of UEs in a cell coverage area of abase station in the wireless communication system.

Example 7

The method as recited in any one of the examples 1 to 6, wherein thedetermining the configuration is based, at least in part, on fulfillingone or more content requirements of the broadcast or multicastcommunications.

Example 8

The method as recited in example 7, wherein the one or more contentrequirements comprise at least one of: a quality requirement; aresolution requirement; or a frames-per-second requirement.

Example 9

The method as recited in any one of the examples 1 to 8, wherein thedetermining the configuration comprises: determining an end-to-endmachine-learning configuration as the configuration of the DNN.

Example 10

The method as recited in example 1, wherein the targeted group of UEsincludes all UEs in a cell coverage area of a base station of thewireless communication system.

Example 11

A method performed by a network entity associated with a wirelesscommunication system, the method comprising: processing broadcast ormulticast communications using a deep neural network (DNN) to direct theone or more broadcast or multicast communications to a targeted group ofuser equipments (UEs) using the wireless communication system; receivingfeedback from at least one user equipment (UE) of the targeted group ofUEs; determining a modification to the DNN based on the feedback;transmitting an indication of the modification to the targeted group ofUEs; updating the DNN with the modification to form a modified DNN; andprocessing the broadcast or multicast communications using the modifiedDNN to direct the broadcast or multicast communications to the targetedgroup of UEs using the wireless communication system.

Example 12

The method as recited in example 11, wherein the transmitting theindication of the modification further comprises: transmitting a firstindication of a first neural network formation configuration to a firstUE of the targeted group of UEs, the first neural network formationconfiguration corresponding to updating parameter configurations of afirst respective UE-side DNN; and transmitting a second indication of asecond neural network formation configuration to a second UE of thetargeted group of UEs, the second neural network formation configurationcorresponding to updating parameter configurations of a secondrespective UE-side DNN, wherein: the first respective UE-side DNN uses afirst DNN architecture, the second respective UE-side DNN uses a secondDNN architecture, and the second DNN architecture is configured as agradient version of the first DNN architecture that performs lessprocessing relative to the first DNN architecture.

Example 13

The method as recited in example 12, further comprising: determining thegradient version of the first DNN architecture based, at least in part,on one or more capabilities associated with the second UE.

Example 14

The method as recited in any one of the examples 11 to 13, wherein thetransmitting the indication of the modification further comprises:transmitting, to each UE of the targeted group of UEs, a neural networkformation configuration that includes an architecture configurationchange to each respective UE-side DNN.

Example 15

The method as recited in any one of the examples 11 to 14, wherein thedetermining the modification to the DNN further comprises: identifying,for each UE in the targeted group of UEs, a respective cost functionthreshold value; determining, based on the feedback, that at least oneUE of the targeted group of UEs fails to meet the respective costfunction threshold value; and determining an architecture configurationchange as the modification to the DNN.

Example 16

The method as recited in example 15, wherein determining thearchitecture configuration change further comprises: determining, for atleast one UE of the targeted group of UEs, at least one architectureconfiguration change to a respective UE-side DNN at the at least one UE,and wherein updating the DNN with the modification further comprises:transmitting an indication of the at least one architectureconfiguration change to at the at least one UE.

Example 17

A network entity apparatus comprising: a wireless transceiver; aprocessor; and computer-readable storage media comprising instructionsthat direct the network entity apparatus to perform operationscomprising: determining a configuration of a deep neural network (DNN)for processing broadcast or multicast communications transmitted over awireless communication system and to a targeted group of user equipments(UEs); forming, at the network entity apparatus, a network-entity DNNbased on the determined configuration of the DNN; and processing thebroadcast or multicast communications using the network-entity DNN todirect the broadcast or multicast communications to the targeted groupof UEs using the wireless communication system.

Example 18

The network entity apparatus as recited in example 17, the operationsfurther comprising: receiving feedback from at least one user equipment(UE) of the targeted group of UEs; determining a modification to the DNNbased on the feedback; updating the DNN with the modification to form amodified DNN; and processing the broadcast or multicast communicationsusing the modified DNN to direct the broadcast or multicastcommunications to the targeted group of UEs.

Example 19

The network entity apparatus as recited in example 17 or example 18,wherein the determining the configuration of the DNN further comprises:determining the configuration of the DNN based, at least in part, ontransmitting broadcast or multicast communications based on a networkslice configuration.

Example 20

The network entity apparatus as recited in any one of the examples 17 to19, wherein the determining the configuration of the DNN furthercomprises: determining an end-to-end machine-learning configuration (E2EML configuration) as the configuration of the DNN, wherein determiningthe E2E ML configuration comprises determining a partitioning to the E2EML configuration that distributes the E2E ML configuration acrossmultiple devices.

Example 21

The network entity apparatus as recited in any one of the examples 17 to20, wherein the determining the configuration of the DNN is based, atleast in part, on one or more quality-of-service (QoS) requirements.

What is claimed is:
 1. A method performed by a network entity associatedwith a wireless communication system, the method comprising: receivinguser equipment (UE) capabilities of a first UE and a second UE in atargeted group of UEs; determining the second UE has less processingpower relative to the first UE based on the UE capabilities; determininga first configuration and a second configuration of a deep neuralnetwork (DNN) for processing broadcast or multicast communicationstransmitted over the wireless communication system to the first UE andthe second UE respectively, the second configuration being a gradientversion of the first configuration of the DNN based on the determiningthe second UE has less processing power relative to the first UE;communicating the first configuration and the second configuration tothe first UE and the second UE respectively; forming, at the networkentity, a network-entity DNN based on the determined first configurationand second configuration of the DNN; and processing the broadcast ormulticast communications using the network-entity DNN to direct thebroadcast or multicast communications to the targeted group of UEs usingthe wireless communication system.
 2. The method as recited in claim 1,wherein the second configuration performs less processing relative tothe first configuration based on the UE capabilities.
 3. The method asrecited in claim 1, wherein the determining the first configuration andthe second configuration comprises: determining the first configurationand the second configuration of the DNN based, at least in part, on atleast one characteristic of the targeted group of UEs, and wherein theat least one characteristic comprises at least one of: an estimatedlocation of at least one UE in the targeted group of UEs.
 4. The methodas recited in claim 1, wherein the UE capabilities indicate processingcapabilities.
 5. The method as recited in claim 1, wherein processingthe broadcast or multicast communications using the network-entity DNNfurther comprises: processing the broadcast or multicast communicationsusing a common DNN as the network-entity DNN to direct the broadcast ormulticast communications to the targeted group of UEs.
 6. The method asrecited in claim 1, wherein the targeted group of UEs is a subset of UEsin a cell coverage area of the wireless communication system.
 7. Themethod as recited in claim 1, wherein the determining the firstconfiguration and the second configuration is based, at least in part,on fulfilling one or more content requirements of the broadcast ormulticast communications.
 8. The method as recited in claim 7, whereinthe one or more content requirements comprise at least one of: a qualityrequirement; a resolution requirement; or a frames-per-secondrequirement.
 9. The method as recited in claim 1, wherein thedetermining the first configuration and the second configurationcomprises: determining an end-to-end machine-learning configuration as atype of the first configuration and the second configuration of the DNN.10. A network entity apparatus comprising: a wireless transceiver; aprocessor; and computer-readable storage media comprising instructionsthat, when executed by the processor, cause the processor to: receiveuser equipment (UE) capabilities of a first UE and a second UE in atargeted group of UEs; determining the second UE has less processingpower relative to the first UE based on the UE capabilities; determine afirst configuration and a second configuration of a deep neural network(DNN) for processing broadcast or multicast communications transmittedover a wireless communication system and to the first UE and the secondUE respectively, the second configuration being a gradient version ofthe first configuration based on the determining the second UE has lessprocessing power relative to the first UE; communicate the firstconfiguration and the second configuration to the first UE and thesecond UE respectively; form, at the network entity apparatus, anetwork-entity DNN based on the determined first configuration andsecond configuration of the DNN; and process the broadcast or multicastcommunications using the network-entity DNN to direct the broadcast ormulticast communications to the targeted group of UEs using the wirelesscommunication system.
 11. The network entity apparatus as recited inclaim 10, wherein the processor is further to: receive feedback from thefirst UE and the second UE of the targeted group of UEs; update thenetwork-entity DNN based on the feedback to form a modifiednetwork-entity DNN; and process the broadcast or multicastcommunications using the modified network-entity DNN to direct thebroadcast or multicast communications to the targeted group of UEs. 12.The network entity apparatus as recited in claim 10, wherein, todetermine the first configuration and the second configuration of theDNN, the processor is further to: determine the first configuration andthe second configuration of the DNN based, at least in part, ontransmitting broadcast or multicast communications based on a networkslice configuration.
 13. The network entity apparatus as recited inclaim 10, wherein, to determine the first configuration and the secondconfiguration of the DNN, the processor is further to: determine anend-to-end machine-learning (E2E ML) configuration as a type of thefirst configuration and the second configuration of the DNN, wherein, todetermine the E2E ML configuration, the processor is further todetermine a partitioning to the E2E ML configuration that distributesthe E2E ML configuration across multiple devices.
 14. The network entityapparatus as recited in claim 10, wherein the configuration firstconfiguration and the second of the DNN are determined based, at leastin part, on one or more quality-of-service (QoS) requirements.
 15. Thenetwork entity apparatus as recited in claim 10, wherein the secondconfiguration performs less processing relative to the firstconfiguration based on the UE capabilities.
 16. A non-transitorymachine-readable medium having instructions stored therein, which whenexecuted by a processor, cause the processor to: receive user equipment(UE) capabilities of a first UE and a second UE in a targeted group ofUEs; determining the second UE has less processing power relative to thefirst UE based on the UE capabilities; determine a first configurationand a second configuration of a deep neural network (DNN) for processingbroadcast or multicast communications transmitted over the wirelesscommunication system to the first UE and the second UE respectively, thesecond configuration being a gradient version of the first configurationof the DNN based on the determining the second UE has less processingpower relative to the first UE; communicate the first configuration andthe second configuration to the first UE and the second UE respectively;form, at the network entity, a network-entity DNN based on thedetermined first configuration and second configuration of the DNN; andprocess the broadcast or multicast communications using thenetwork-entity DNN to direct the broadcast or multicast communicationsto the targeted group of UEs using the wireless communication system.17. The non-transitory machine-readable medium as recited in claim 16,wherein the processor is further to: determine the first configurationand the second configuration of the DNN based, at least in part, on atleast one characteristic of the targeted group of UEs.
 18. Thenon-transitory machine-readable medium as recited in claim 17, whereinthe at least one characteristic comprises: an estimated location of atleast one UE in the targeted group of UEs.
 19. The non-transitorymachine-readable medium as recited in claim 16, wherein the UEcapabilities indicate processing capabilities.
 20. The non-transitorymachine-readable medium as recited in claim 16, wherein the secondconfiguration performs less processing relative to the firstconfiguration based on the UE capabilities.